diff --git a/jupyter_notebooks/Traces.ipynb b/jupyter_notebooks/Traces.ipynb index 95ec8b4..613766b 100644 --- a/jupyter_notebooks/Traces.ipynb +++ b/jupyter_notebooks/Traces.ipynb @@ -1536,7 +1536,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.13.5" + "version": "3.13.3" } }, "nbformat": 4, diff --git a/jupyter_notebooks/classifier_tests.ipynb b/jupyter_notebooks/classifier_tests.ipynb index ce6c94d..25ac170 100644 --- a/jupyter_notebooks/classifier_tests.ipynb +++ b/jupyter_notebooks/classifier_tests.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "id": "a20b8991-06b8-4686-b296-aeb0d177125b", "metadata": {}, "outputs": [], @@ -27,7 +27,7 @@ }, { "cell_type": "code", - "execution_count": 133, + "execution_count": 3, "id": "32a509a0-ff60-4744-b0a6-7077c4a15bec", "metadata": {}, "outputs": [ @@ -35,9 +35,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_97645/2846935186.py:45: UserWarning: Unknown pitch for 'cal-open'\n", + "/tmp/ipykernel_11621/2846935186.py:45: UserWarning: Unknown pitch for 'cal-open'\n", " warnings.warn(f'Unknown pitch for {meta!r}')\n", - "/tmp/ipykernel_97645/2846935186.py:45: UserWarning: Unknown pitch for 'baseline-5-open'\n", + "/tmp/ipykernel_11621/2846935186.py:45: UserWarning: Unknown pitch for 'baseline-5-open'\n", " warnings.warn(f'Unknown pitch for {meta!r}')\n" ] } @@ -112,7 +112,7 @@ }, { "cell_type": "code", - "execution_count": 132, + "execution_count": 4, "id": "26ba6967-a405-49d5-b835-c3cb9276bc08", "metadata": { "scrolled": true @@ -121,21 +121,21 @@ { "data": { "text/plain": [ - "1182" + "1264" ] }, - "execution_count": 132, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "sum(len(value) for key, value in data.items())" + "sum(len(value) for key, value in data_runs.items())" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "id": "62650fb7-6230-4883-ba9a-aad9e0c4b69e", "metadata": { "scrolled": true @@ -147,6 +147,11 @@ "Counter({'Baseline-2-p0.3': 100,\n", " 'attack-repeat-25-07-09-01-p0.3': 100,\n", " 'copy-interleave-25-07-11-01-p0.3': 63,\n", + " 'short-within-trace-interleave-reference-25-07-11-04-p0.3': 42,\n", + " 'apply-patch-interleave-reference-25-07-11-03-p0.3': 40,\n", + " 'apply-patch-interleave-modified-25-07-11-03-p0.3': 40,\n", + " 'short-within-trace-interleave-modified-25-07-11-04-p0.3': 40,\n", + " 'patch-interleave-25-07-11-02-p0.3': 40,\n", " 'simple-attack-25-07-07-01-p0.3': 33,\n", " 'simple-attack-25-07-07-01-p0.4': 33,\n", " 'Baseline-1-p0.3': 30,\n", @@ -206,7 +211,7 @@ " 'baseline-5-open': 1})" ] }, - "execution_count": 4, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -217,7 +222,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "id": "50d21521-28d4-44da-b72f-223212424c9d", "metadata": {}, "outputs": [ @@ -253,7 +258,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "id": "eda2dc4b-1641-4fce-a4ec-cba08d8df9f9", "metadata": { "scrolled": true @@ -440,7 +445,7 @@ " 'baseline': False}]" ] }, - "execution_count": 6, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -451,7 +456,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "id": "54565f84-158c-4a91-9420-3b047c46b369", "metadata": { "scrolled": true @@ -482,7 +487,7 @@ " '2025-07-01T13:20:32.218Z']}" ] }, - "execution_count": 7, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -522,7 +527,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "id": "b8d7c4e0-7860-4058-8fd1-e7948ac06674", "metadata": {}, "outputs": [ @@ -532,7 +537,7 @@ "5" ] }, - "execution_count": 8, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -544,7 +549,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "id": "72b6defa-c833-4f5e-9166-42d0654625bc", "metadata": {}, "outputs": [ @@ -692,7 +697,7 @@ }, { "cell_type": "code", - "execution_count": 116, + "execution_count": 11, "id": "6f435bd9-ebbc-458d-bad6-044ca7d7a111", "metadata": {}, "outputs": [ @@ -721,7 +726,7 @@ " )" ] }, - "execution_count": 116, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, @@ -1896,7 +1901,7 @@ }, { "cell_type": "code", - "execution_count": 125, + "execution_count": 37, "id": "a9fce3aa-2108-48e5-afae-c3f1e75c7db3", "metadata": {}, "outputs": [ @@ -1904,6 +1909,7 @@ "name": "stdout", "output_type": "stream", "text": [ + "{1, 2, 3, 4, 5, 6, 7, 8, 9, 10}\n", "setting threshold for quantile 0.001\n", "Baseline threshold set at 0.974230\n", "\n", @@ -1934,13 +1940,13 @@ " )" ] }, - "execution_count": 125, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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n53d9jo79j3uPU3GfWs8viP/xKxfQsTd8l//mXnbpeVRcuUhsMFGs4Ef/Rx8awFhHta2z2tqZHQFqEYnEmDqrRcQ5tYhExDm1iETEuYRHdFZHfxMPJSKROPOIFpFuzUSkrtRHJCLOqY9IRJxTIhIR5xK+h4Ql09hejwIlIpEYUx/RuIfXfAuphmZr3N4kOR0hzFK5IWL9E8Tq5QCwjh/ebwwf64X4NFRe5BbFNwF//EL6FTqWXeg+zLSNXZ//NR37r/OnX7h9wp13vU7XeffRVXTs7VsO0rHf+MUKKq7xpH2KTbka/jm7748VW0zUqUUkEmPqIxIR5yb2GbPFRJ0SkUiM+QkgYel18N8Py4CIiDu6NRMR53RrJiLO+SBaROfkTGZHiUgkxjx/rNhiok6JSCTGkr6HpKVJZHs9CpSIRGJMLSIRcU6d1eOufvl73C4e69uo+gb7+V08Rgb5XTw+evn5VNz+h3rpOlNp/j81Hcvs02AmLFvfSsU1ZvldPPa88DIdu2t0PhXH7rYB8NM2AOD+v/klFfcP/7OJrvOv//4wHXvD1wbp2G9fe4SKM8TsjeEisOff6EMD0ON7EYkA3ZqJiHM+sQyIH4MmkRKRSIxp9r2IOKfOahFxTgujiYhzvu9Z+4DURyQidZXwPSQSljWrq0pEIlJHujUTEeeUiMbt/UMRqUTVGvfz352k6utID9HHzjbSobhvT4GKe8tfQNfZUOFH4K7M8CO2g59ysdkm/hO2r38xHfvh5jwV96n1LXSdYRa6Z0dM/77rYbrO/f94KR37t2tSdOxthz5DxXl/fsoaU67yMwUmqI9IRJxTi0hEnPMTHnxLZ7Xt9ShQIhKJMd/z4FuaPLbXo0CJSCTOiEmvcVgrlk5ExWIRxeI7O5AWClznr4jUz1zpI6JzZXd3N7LZ7GTJ5XL1PC8RIXi+R5WooxNRV1cXBgYGJks+zz3iFZH6SXhjy4BMW2LQJKJvzdLpNNLpdD3PRURC0sJoIuLcXFkGJAa5UkTey8TCaLZSD6+99hq2bNmC5cuXo7GxEStWrMC2bdtQKvHrzk+YdYuob9kmzJvXYI3zG7nF418aOEEf2xvkY7GC61z3m7mF4wGg0M8vHn+QWT19nJdu4gL7j9F1mgX8dIyeN96m4j5+JT8d5u6jq+hYdqH7MNM2qg/+no799P4v0bG7duyj4hLVUWtMMIMpHmNPzWwtotDVUl588UUEQYB7770XK1euxPPPP4+vfOUrGB4exh133BGqLt2aicRYmJHV7x5yM9t+302bNmHTpnfmBV544YU4dOgQ7rnnntCJSLdmIjEW5tYsl8tNGYLT3d1d8/MZGBhAWxu3ddjp1CISiTFmnNDE6/l8HplMZvLntX4K/sorr+AHP/gB7rzzztC/qxaRSIxNjKy2FQDIZDJTynslou3bt08+jXuvcuDAgSm/09vbi02bNuGaa67B9ddfH/o61CISibEwLSLWzTffjOuum35n3mXLlk3+/97eXlxxxRXYsGEDdu7cGepYE5SIRGLMJ9asDrswWnt7O9rb26nYN954A1dccQXWrl2Ln/zkJ/BnOFZAiUgkxlxOeu3t7cXGjRuxZMkS3HHHHXjzzTcnX+vo6AhVlxKRSIzV49aM9cgjj+Dw4cM4fPgwFi+euhSxMSZUXeqsFokxl7Pvv/zlL8MYc9YSllpEIjE2V9YjmnUi+kL2cTSl7NV8ZG0rVd9gPz9PZbhQoWM/elk/Fbd/Lz9tYt5yvkG5aBk3xQUAll6UpeKaWufRdf54xwF70LjH0kupuBu+y/+X7/YtB+nYG77G7Y4SZreNMNM2fvTJB+jYGz/5aSqusSlhjRkuVvDQi/ShAQCJpIdE0rLBouX1KFCLSCTG5srseyUikRjTekQi4pzLp2a1pEQkEmO+N1ZsMVGnRCQSY37Ch5+c/t7LT0T/3kyJSCTG9PheRNxjBizG4N5MiUgkxpg1qeu1ZnUtKRGJxJiemomIc2HWrI6yWSein/V9AvOS9iUn/Rf6qPoMUddknaVhOjZ45jzy+IvoOsOMFPMPvGkPmvAg98FJjnK7bQBAObOOjr0s+zQXdyn3ngLAN36xgo799rVHqLjbDn2GrpPdbQPgp20AwNPb/ouKe+a6LmtMuVIE8Bh9bEAtIhGJAPURiYhzHoi5ZlCLSETqSHPNRMS5RNJHwjKy2vZ6FCgRicSYWkQi4txYIrI9NTtHJzMLSkQiMabH9yLinCa9iohzPrFmtf9+WLM68XYeiYR9EfNEZYSqb97IKH1sE2J8RNOpo1RcIXk+f3zfviD6hLYyvyj/h9rLVNzB4Ta6TjPYS8ceJy+rXAzoOhtPvkrHGrJa789P0XUmqvznilnofgIzYhoA1v+y2xozOljAI391F31sYHzNatutWQyaRGoRicSY+ohExDlN8RAR59QiEhHn/KRn7Yx+X3RWi4g7vu/Bt7R4bK9HgRKRSIzp1kxEnNNcMxFxTuOIRMS5BDGy2vZ6FMw4ERljAADlKjcKOCDjwMYh3MjqkqlQcWUvxPHZIcAASlXu+AAwWiHPNcR7Fea6iuCOP1zkr6lc5d+r4SJbJ39N9OcPIa+rwp3s6GDBGlMcGouZ+G4xBk+VrS2iwVP8tbvimTBXfZrXX38duVyu1ucj8r6Xz+exePHiaWNGR0exfPly9PVxm1J0dHTgyJEjaGhoqMUp1tyME1EQBOjt7UVLS8vkPWihUEAul0M+n0cmk6npibo0F69L1xQ9xhgMDg5i0aJF8Inh0KOjoyiVSlTdqVQqskkImMWtme/775m1M5lMLD8INnPxunRN0ZLNZunYhoaGSCeXMGLwYE9E5jolIhFxrqaJKJ1OY9u2bUin+d1a42AuXpeuSaJkxp3VIiK1olszEXFOiUhEnFMiEhHnlIhExDklIhFxTolIRJxTIhIR55SIRMS5/wd1yeXLgL5ihwAAAABJRU5ErkJggg==", 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" ] @@ -1974,16 +1980,18 @@ "group_baseline = [value for value in entries if not value['info']['mods']]\n", "group_modified = [value for value in entries if value['info']['mods']]\n", "\n", + "print(set(value['info']['serial'] for value in group_baseline))\n", + "\n", "smooth = None\n", "plot_vector_covar(group_baseline, group_modified, vector_method='norm', distance_method='corr', smooth=smooth)\n", "#plot_vector_covar(group_baseline, group_modified, vector_method='min', distance_method='corr', smooth=smooth)\n", "#plot_vector_covar(group_baseline, group_modified, vector_method='norm', distance_method='max', smooth=smooth)\n", - "plot_vector_covar(group_baseline, group_modified, vector_method='min', distance_method='max', smooth=smooth, plot_cdf=True)" + "plot_vector_covar(group_baseline, group_modified, vector_method='min', distance_method='max', smooth=None, plot_cdf=True)" ] }, { "cell_type": "code", - "execution_count": 123, + "execution_count": 41, "id": "3e0f0a08-205f-4621-a366-9f80b88ed5ce", "metadata": {}, "outputs": [ @@ -1992,6 +2000,7 @@ "output_type": "stream", "text": [ "[['short_within_trace'], ['short_within_trace'], ['short_within_trace'], ['short_within_trace'], ['short_within_trace'], ['short_within_trace'], ['short_within_trace'], ['short_within_trace'], ['short_within_trace'], ['short_within_trace']]\n", + "{1, 2, 3, 4, 5, 6, 7, 8, 9, 10}\n", "setting threshold for quantile 0.001\n", "Baseline threshold set at 0.991740\n", "\n", @@ -2001,6 +2010,7 @@ "\n", "Type 1 error (false alarm rate): 0.001000000000\n", "Type 2 error (missed alarm rate): 0.231220998491\n", + "EER: 0.16842105263157894 th: 0.9943917901819603\n", "setting threshold for quantile 0.001\n", "Baseline threshold set at 0.447921\n", "\n", @@ -2022,13 +2032,13 @@ " )" ] }, - "execution_count": 123, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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rAICVK1di9uzZWtt2eurpz1OnTqFPnz54+PAhEhMTMXHiRPznP/9B3759WxRbamoq4uPjkZiYiKFDh+Ljjz9GeHg4lEolfH19m9ymuroa3bp1w5IlS7Bhw4Ym16mpqcErr7wCV1dXHD58GN7e3igsLNT6fT788ENs3boVe/bsQZ8+fXDx4kVMnz4dTk5OiIuLe27s4eFAYqIEEqdqs6ih8NzGzCjCw8PJy8uLHj16pFP2008/ERGRn58fbdiw4ZmfkZeXRwAoJydHs6ysrIwA0KZNm1ocW0hICM2dO1drWVBQECUkJOi1/YgRIyguLk5neVJSEgUEBFBNTc0ztx0zZgzNmDFDa9mECRNo2rRpeu37wgVhvuUvXV6j62+8QXV1dXptZyh9z1u+5GFt7sGDBzh+/DhiY2Nh38Qgys7Ozi363NraWmzfvh0AIJfLNcszMjLg4ODQ7Gv16tUAhFpEdnY2wsLCtD47LCwMFy5caFFcakePHkVoaChiY2Ph5uaGvn37YvXq1VA1GnZt2LBh+PLLL5H7ZE6Ny5cvIzMzE6NHj9ZrHyEhgJ8foaDS3SxqKHzJw9rcjRs3QEQICgp67rp//OMfsXTpUq1laWlpGDlypOb9kCFDIJVKUVlZifr6evj7+2Py5Mma8kGDBuHSpUvN7qdLly4AgB9//BEqlQpubm5a5W5ubigpKXluvM25desWTp8+jcjISKSnp+P69euIjY1FXV0dli9fDkD4fUtLSxEUFASZTAaVSoVVq1ZhypQpeu1DJgOuX6+HxGk/rimmtipeMXBCYW2OngwxJtFjopmFCxciJiZGa5mXl5fW+9TUVAQFBSE3Nxfx8fHYunWrJkEAgEKhwAsvvGBQjE/HRkR6xduc+vp6uLq6Ytu2bZDJZAgODkZRURHWrVunSSipqanYt28fDhw4gD59+uDSpUuIj4+Hp6cnoqOj9dqPtKYKVZUSlMsdWxWvGDihsDbXs2dPSCQSXLlyBePHj292XRcXl+cmAx8fH/Ts2RM9e/aEg4MDJk6cCKVSCVdXVwDCJU94eHizn7F48WIsXrwYLi4ukMlkOrWRe/fu6dRaDOXh4QG5XA6ZTKZZ1qtXL5SUlKCmpgbW1tZYuHAhEhIS8JsnA8X+7Gc/w+3bt7FmzRq9E0rZqSx44X+If5SJn7cq4tbjhMLaXJcuXTBq1Chs2bIF8+fP12lHefjwYYvbUUaMGIG+ffti1apV2LhxIwDDLnmsra0RHByMkydP4o033tCUnzx5EuPGjWtRTGpDhw7FgQMHUF9fD+mT0aZzc3Ph4eEBa2trAEBFRYWmTE0mk+l121jNKesUXpC64N/Fga2KVxRt0iTcDL7L0zHdunWL3N3dqXfv3nT48GHKzc0lpVJJGzdupKCgICIS7vKsXLmSiouLtV7q/ytN3eUhIjp69CjZ2NjQnTt3WhRbSkoKyeVy2rlzJymVSoqPjyd7e3vKz8/XrJOQkEBRUVFa2+Xk5FBOTg4FBwfT1KlTKScnh77//ntNeUFBATk4ONC8efPo2rVrlJaWRq6urvT+++9r1omOjiYvLy9KS0ujvLw8+vvf/04uLi60aNEiveOvDw2lmR6fkpvbI5Pf5eGEwoymqKiIYmNjyc/Pj6ytrcnLy4tef/11OnPmDBEJCQWAzmvOnDlE9OyEUl9fT4GBgfTmm2+2OLYtW7Zo4ho4cCCdO3dOqzw6OppGjBihtaypWP38/LTWuXDhAv3iF78gGxsbCggIoFWrVmmd9GVlZRQXF0e+vr5ka2tLAQEBtGTJEqqurtYv8LIyqreyovWjthJAdPOmaROKhKipQfnbTllZGZycnFBaWgpHR9M3IjFm0T7/HBg9GkfWJWNSQjQOHyaMHy97/nYG0ve85edQGLNkp0+DvLxg9WJXHDhwHGPHmjacFiUUQ/s9MMbayOnToJdfBiQSKBSqJicBMyaDE4q638OSJUuQk5OD4cOHIzw8HAUFBW0RH2PsWR48AHJygJdfBgDk5jrDw0OK27dNF5LBCWX9+vWYOXMmZs2ahV69euGjjz6Cj48PkpKS2iI+xlhTamuBjz4CiEBPniL28HiM+/eBc+dMF5ZBz6Go+z0kJCRoLW+u30N1dTWqq6s178vUg2Bu2waYQd8D1gZ+burHq9q5zEzg44+B69eB+fMBPz/g++/RqVMt+vUDkpOB8nIgNlZY/fBh4O5d7Y/41a+AwEDg22+Bp1ssfH2BsWOB6mphgnYA0PcxIYMSSkv6PaxZswYrVqzQLVi8uGEmaNa+PNUXh4ns/feBkSOBTz8F+vcXxoB8YtIkwsqVEnz1VUNC2bgRyMrS/ojdu4WEkpkpzJncWFiYkFAqKxvKhgzRLzSDbhsXFRXBy8sLFy5cQGhoqGb5qlWrsHfvXs2YFo01VUPx8fHh28aMiUSlUmkGbRo9erTWo/5i0fe2sUE1lJb0e7CxsYFNo8mH1PlLc+nDGGsVlUqFiooKAMJ51VYJBWg4f5/FoIQiRr+H8vJyAEIHL8aYZSkvL4eTk9Mzyw3uHLhgwQJERUVh0KBBCA0NxbZt21BQUIC5c+fqtb2np6dmGLzWdg+3NOrLvcLCQr7c0xMfs5YR+7gREcrLy+Hp6dnsegYnlIiICNy/fx8rV65EcXEx+vbti/T0dPj5+em1vVQqhbe3t6G7bVccHR355DAQH7OWEfO4NVczUTN6X56OjPsxGY6PWcuY6rhxXx7GmGg4oRiRjY0N3n33Xa27Xqx5fMxaxlTHjS95GGOi4RoKY0w0nFAYY6LhhMIYEw0nFMaYaDihiMjQkez279+P/v37w87ODh4eHpg+fTru379vpGjNh6HHbcuWLejVqxcUCgUCAwPxySefGClS83D+/HmMHTsWnp6ekEgk+Oyzz567zblz5xAcHAxbW1sEBARg69atbRNcmwyR3QGpp2LYvn07KZVKiouLI3t7e7p9+3aT62dkZJBUKqWNGzfSrVu3KCMjg/r06UPjx483cuSmZehxS0xMpE6dOlFKSgrdvHmTDh48SA4ODnT06FEjR2466enptGTJEvrb3/5GAOjIkSPNrn/r1i2ys7OjuLg4UiqVtH37dpLL5XT48GHRY+OEIpKQkBCaO3eu1rKgoCBKSEhocv1169ZRQECA1rJNmzaRt7d3m8Vojgw9bqGhofT2229rLYuLi6OhQ4e2WYzmTJ+EsmjRIs3cR2pz5syhwYMHix4PX/KIQD2SXVhYmNby5kayGzJkCO7cuYP09HQQEe7evYvDhw9jzJgxxgjZLLTkuFVXV8PW1lZrmUKhQFZWFmpra9ssVkv21Vdf6RzjUaNG4eLFi6IfM04oImjJSHZDhgzB/v37ERERAWtra7i7u8PZ2RmbN282RshmoSXHbdSoUdixYweys7NBRLh48SKSk5NRW1uLH3/80RhhW5ySkpImj3FdXZ3ox4wTioieHo6BiJ45RINSqcT8+fOxfPlyZGdn4/jx48jLy9N7GIj2xJDjtmzZMoSHh2Pw4MGQy+UYN24cYmJiAKBNBhZqL5o6xk0tby1OKCJoyUh2a9aswdChQ7Fw4UL069cPo0aNQmJiIpKTk1FcXGyMsE2uJcdNoVAgOTkZFRUVyM/PR0FBAfz9/dGpUye4uLgYI2yL4+7u3uQxtrKyQteuXUXdFycUETQeya6xkydPYsgzRvetqKiAVKp9+NV/YamDdK9qyXFTk8vl8Pb2hkwmQ0pKCl577TWd48kEoaGhOsf4xIkTGDRoEORyubg7E72Zt4NS3/7cuXMnKZVKio+PJ3t7e8rPzyciooSEBIqKitKsv2vXLrKysqLExES6efMmZWZm0qBBgygkJMRUv4JJGHrcrl27Rnv37qXc3Fz65ptvKCIigrp06UJ5eXkm+g2Mr7y8nHJycignJ4cA0Pr16yknJ0dzq/3pY6a+bfzWW2+RUqmknTt38m1jS7Blyxby8/Mja2trGjhwIJ07d05TFh0dTSNGjNBaf9OmTdS7d29SKBTk4eFBkZGRdOfOHSNHbXqGHDelUkkvvfQSKRQKcnR0pHHjxtHVq1dNELXpnDlzhgDovKKjo4mo6f9rZ8+epQEDBpC1tTX5+/tTUlJSm8TGwxcwxkTDF52MMdFwQmGMiYYTCmNMNJxQGGOi4YTCGBMNJxTGmGg4oTDGRMMJhTEmGk4ojDHRcEJhjImGEwpjTDScUBhjovl/dINEJvT4hIYAAAAASUVORK5CYII=", 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", 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" ] @@ -2062,9 +2082,10 @@ "group_baseline = [value for value in entries if not value['info']['mods']]\n", "group_modified = [value for value in entries if value['info']['mods']]\n", "print([value['info']['mods'] for value in group_modified])\n", + "print(set(value['info']['serial'] for value in group_baseline))\n", "\n", "smooth = None\n", - "plot_vector_covar(group_baseline, group_modified, vector_method='norm', distance_method='corr', fig_out='short_within_0.3', smooth=smooth)\n", + "plot_vector_covar(group_baseline, group_modified, vector_method='norm', distance_method='corr', fig_out='short_within_0.3', plot_cdf=True, smooth=smooth)\n", "#plot_vector_covar(group_baseline, group_modified, vector_method='min', distance_method='corr', smooth=smooth)\n", "#plot_vector_covar(group_baseline, group_modified, vector_method='norm', distance_method='max', smooth=smooth)\n", "plot_vector_covar(group_baseline, group_modified, vector_method='min', distance_method='max', smooth=smooth, plot_cdf=True, fig_out='short_within_0.3_min_max', do_lines=True)" @@ -2072,7 +2093,7 @@ }, { "cell_type": "code", - "execution_count": 124, + "execution_count": 34, "id": "72805716-cee3-4a1b-94c8-d810756b8c18", "metadata": {}, "outputs": [ @@ -2082,6 +2103,7 @@ "text": [ "tdp0604 tdp0604\n", "[['short_within_trace'], ['short_within_trace'], ['short_within_trace'], ['short_within_trace'], ['short_within_trace'], ['short_within_trace'], ['short_within_trace'], ['short_within_trace'], ['short_within_trace'], ['short_within_trace']]\n", + "{10}\n", "setting threshold for quantile 0.001\n", "Baseline threshold set at 0.994850\n", "\n", @@ -2108,13 +2130,13 @@ "(
, , None, None)" ] }, - "execution_count": 124, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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", 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", 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" ] @@ -2140,6 +2162,7 @@ "group_baseline = [value for value in entries_repeat if not value['info']['mods']][:10]\n", "group_modified = [value for value in entries if value['info']['mods']]\n", "print([value['info']['mods'] for value in group_modified])\n", + "print(set(value['info']['serial'] for value in group_baseline))\n", "\n", "smooth = None\n", "plot_vector_covar(group_baseline, group_modified, vector_method='norm', distance_method='corr', smooth=smooth)\n", @@ -2837,7 +2860,7 @@ }, { "cell_type": "code", - "execution_count": 117, + "execution_count": 18, "id": "b431b6fb-2acf-4d52-90ba-6ea754ee1d6c", "metadata": {}, "outputs": [ @@ -2972,21 +2995,7 @@ "\n", "Type 1 error (false alarm rate): 0.001000000000\n", "Type 2 error (missed alarm rate): 0.999848317762\n", - "EER: 0.6333333333333333 th: 0.9697796637264954\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_162994/1350806041.py:109: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). Consider using `matplotlib.pyplot.close()`.\n", - " fig, ax = plt.subplots(figsize=figsize)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "EER: 0.6333333333333333 th: 0.9697796637264954\n", "setting threshold for quantile 0.001\n", "Baseline threshold set at -0.044841\n", "\n", @@ -2995,7 +3004,21 @@ "Cross class: 0.254±0.0908 min: 0.0437 max: 0.397\n", "\n", "Type 1 error (false alarm rate): 0.001000000000\n", - "Type 2 error (missed alarm rate): 0.999511411088\n", + "Type 2 error (missed alarm rate): 0.999511411088\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_11621/528344108.py:124: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). Consider using `matplotlib.pyplot.close()`.\n", + " fig, ax = plt.subplots(figsize=figsize)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "EER: 0.4923076923076923 th: 0.2817379300134156\n" ] }, @@ -3259,7 +3282,9 @@ { "cell_type": "markdown", "id": "559e538d-efb6-4500-8f32-757cc8ba6f35", - "metadata": {}, + "metadata": { + "jp-MarkdownHeadingCollapsed": true + }, "source": [ "## Can we distinguish baseline versus patched traces?\n", "\n", @@ -3505,11 +3530,9 @@ }, { "cell_type": "code", - "execution_count": 149, + "execution_count": 14, "id": "ff523649-f8af-47c0-a2ce-a0433686456f", - "metadata": { - "scrolled": true - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -3675,7 +3698,7 @@ }, { "cell_type": "code", - "execution_count": 150, + "execution_count": 16, "id": "536e703b-4106-447c-a3ae-7483da51ab48", "metadata": {}, "outputs": [ @@ -3684,14 +3707,14 @@ "output_type": "stream", "text": [ "setting threshold for quantile 0.001\n", - "Baseline threshold set at 0.979357\n", + "Baseline threshold set at 0.988544\n", "\n", "Distribution parameters:\n", - "Within class: 0.989±0.00314 min: 0.983 max: 0.993\n", - "Cross class: 0.985±0.00315 min: 0.98 max: 0.991\n", + "Within class: 0.994±0.00162 min: 0.989 max: 0.996\n", + "Cross class: 0.991±0.00145 min: 0.987 max: 0.994\n", "\n", "Type 1 error (false alarm rate): 0.001000000000\n", - "Type 2 error (missed alarm rate): 0.972331318110\n" + "Type 2 error (missed alarm rate): 0.959533969920\n" ] }, { @@ -3700,13 +3723,13 @@ "(
, , None, None)" ] }, - "execution_count": 150, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -3716,17 +3739,19 @@ } ], "source": [ - "batchlabels_before_after = (list(range(5, 45, 10)), ['Reference A (before)', 'Reference A (after)', 'Experiment (before)', 'Experiment (after)'])\n", + "batchlabels_before_after = (list(range(5, 45, 10)), ['Control (before)', 'Control (after)', 'Experiment (before)', 'Experiment (after)'])\n", "fetch = lambda run, serial: [value for value in data_runs[f'apply-patch-interleave-{run}-25-07-11-03-p0.3'] if value['info']['serial'] == serial]\n", - "group_a3 = fetch('reference', 1) + fetch('modified', 1) + fetch('reference', 74)\n", + "group_a3 = fetch('reference', 73) + fetch('modified', 73) + fetch('reference', 74)\n", "group_b3 = fetch('modified', 74)\n", - "plot_vector_covar(group_a3, group_b3, do_lines=False, smooth=1e-10, triangle_delta=False, batchlabels=batchlabels_before_after, figsize=(3, 3), fig_out='patch_ref_exp_interleave_direct', label_placement='x')" + "plot_vector_covar(group_a3, group_b3, do_lines=False, smooth=1e-9, triangle_delta=False, batchlabels=batchlabels_before_after, figsize=(3, 3), fig_out='patch_ref_exp_interleave_direct', label_placement='x')" ] }, { "cell_type": "markdown", "id": "22d21172-9d15-4e10-93d5-e9fbc35a7dcf", - "metadata": {}, + "metadata": { + "jp-MarkdownHeadingCollapsed": true + }, "source": [ "## Can we distinguish a board that has a trace shorted to itself?\n", "\n", @@ -4957,9 +4982,7 @@ { "cell_type": "markdown", "id": "1702f98f-74e2-4e13-b2f3-9276e9112606", - "metadata": { - "jp-MarkdownHeadingCollapsed": true - }, + "metadata": {}, "source": [ "## Repeat looped patch experiment with multiple runs per sample\n", "\n", @@ -4968,7 +4991,7 @@ }, { "cell_type": "code", - "execution_count": 103, + "execution_count": 67, "id": "e93fec13-806b-4181-9bd4-8912a2d9800d", "metadata": {}, "outputs": [ @@ -4976,7 +4999,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "{'SP-PITCH-V2-0058', 'SP-PITCH-V2-0063', 'SP-PITCH-V2-0062', 'SP-PITCH-V2-0061', 'SP-PITCH-V2-0065', 'SP-PITCH-V2-0057', 'SP-PITCH-V2-0060'}\n", + "{'SP-PITCH-V2-0063', 'SP-PITCH-V2-0065', 'SP-PITCH-V2-0062', 'SP-PITCH-V2-0057', 'SP-PITCH-V2-0058', 'SP-PITCH-V2-0061', 'SP-PITCH-V2-0060'}\n", "20 2025-07-09T17:39:02.553Z\n", "70 2025-07-09T17:37:40.678Z\n", "setting threshold for quantile 0.001\n", @@ -5004,21 +5027,21 @@ { "data": { "text/plain": [ - "(
,\n", + "(
,\n", " ,\n", "
,\n", " )" ] }, - "execution_count": 103, + "execution_count": 67, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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", 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", 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" ] }, "metadata": {}, @@ -5065,8 +5088,9 @@ "print(len(group_a), group_a[-1]['info']['timestamp'])\n", "print(len(group_b), group_b[-1]['info']['timestamp'])\n", "\n", - "plot_vector_covar(group_a, group_b, plot_cdf=True, fig_out='patch_repeat_p0.3', do_lines=True, figsize=(5, 5), smooth=1e-7)\n", - "plot_vector_covar(group_a, group_b, vector_method='min', distance_method='max', plot_cdf=True, fig_out='patch_repeat_p0.3_minmax', do_lines=True, figsize=(5, 5), smooth=1e-9)" + "batchlabels = (list(range(5, 90, 10)), ['Baseline (before)', 'Baseline (after)', '', '', '', 'Patching attacks', '', '', ''])\n", + "plot_vector_covar(group_a, group_b, plot_cdf=True, fig_out='patch_repeat_p0.3', do_lines=True, figsize=(3.5,3.5), smooth=1e-7, batchlabels=batchlabels, label_placement='x')\n", + "plot_vector_covar(group_a, group_b, vector_method='min', distance_method='max', plot_cdf=True, fig_out='patch_repeat_p0.3_minmax', do_lines=True, figsize=(3.5,3.5), smooth=1e-9, batchlabels=batchlabels, label_placement='x')" ] }, { @@ -5158,7 +5182,7 @@ }, { "cell_type": "code", - "execution_count": 113, + "execution_count": 63, "id": "dba0e4b8-4207-4f6d-8260-4e5e134cbab6", "metadata": {}, "outputs": [ @@ -5166,49 +5190,49 @@ "name": "stdout", "output_type": "stream", "text": [ - "{'SP-PITCH-V2-0058', 'SP-PITCH-V2-0063', 'SP-PITCH-V2-0062', 'SP-PITCH-V2-0061', 'SP-PITCH-V2-0065', 'SP-PITCH-V2-0057', 'SP-PITCH-V2-0060'}\n", + "{'SP-PITCH-V2-0063', 'SP-PITCH-V2-0065', 'SP-PITCH-V2-0062', 'SP-PITCH-V2-0057', 'SP-PITCH-V2-0058', 'SP-PITCH-V2-0061', 'SP-PITCH-V2-0060'}\n", "70 2025-07-09T17:39:02.553Z\n", - "70 2025-07-09T17:37:40.678Z\n", + "21 2025-07-09T17:37:33.769Z\n", "setting threshold for quantile 0.001\n", - "Baseline threshold set at 0.998952\n", + "Baseline threshold set at 0.978057\n", "\n", "Distribution parameters:\n", - "Within class: 0.999±0.000111 min: 0.999 max: 1\n", - "Cross class: 0.999±0.000444 min: 0.997 max: 0.999\n", + "Within class: 0.984±0.00193 min: 0.978 max: 0.989\n", + "Cross class: 0.98±0.00347 min: 0.966 max: 0.986\n", "\n", "Type 1 error (false alarm rate): 0.001000000000\n", - "Type 2 error (missed alarm rate): 0.354658531438\n", - "EER: 0.1456372180952635 th: 0.9991936057976704\n", + "Type 2 error (missed alarm rate): 0.690844956542\n", + "EER: 0.19512685790021775 th: 0.9823359725826497\n", "setting threshold for quantile 0.001\n", - "Baseline threshold set at 0.328759\n", + "Baseline threshold set at 0.525644\n", "\n", "Distribution parameters:\n", - "Within class: 0.593±0.0855 min: 0.231 max: 0.77\n", - "Cross class: 0.46±0.0965 min: -0.063 max: 0.711\n", + "Within class: 0.745±0.0709 min: 0.443 max: 0.885\n", + "Cross class: 0.567±0.118 min: 0.155 max: 0.805\n", "\n", "Type 1 error (false alarm rate): 0.001000000000\n", - "Type 2 error (missed alarm rate): 0.913000208571\n", - "EER: 0.22598211731369175 th: 0.53232711268001\n" + "Type 2 error (missed alarm rate): 0.635338095839\n", + "EER: 0.16773742265267688 th: 0.6806741308858419\n" ] }, { "data": { "text/plain": [ - "(
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,\n", " ,\n", "
,\n", " )" ] }, - "execution_count": 113, + "execution_count": 63, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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", 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", 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", 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", 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", 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", "text/plain": [ "
" ] @@ -5253,7 +5277,7 @@ " '25-07-08-01-homeoffice-desktop',\n", " '25-07-09-01-forest-x1c',\n", " ]\n", - "batchlabels = (list(range(5, 140, 10)), ['Office', 'Server Room', 'Electronics Lab', 'Home Office', 'Forest', 'Before Experiment', 'After Experiment', '', '', '', 'Patching attacks', '', '', ''])\n", + "batchlabels = (list(range(5, 70, 10)) + [80], ['Office', 'Server Room', 'Electronics Lab', 'Home Office', 'Forest', 'Before Experiment', 'After Experiment', 'Patching attacks'])\n", "group_a = [value for value in data_runs[f'environment-25-07-08-01-office-p14s-p{pitch}'] if not value['info']['mods']]\n", "group_b = [value for env in environments for value in data_runs[f'environment-{env}-p{pitch}'] if not value['info']['mods']]\n", "group_a += group_b\n", @@ -5261,30 +5285,16 @@ "# Exclude mesh 56 as its response is easily distinguished by min/max, making it light up very bright in the plot and swamping out the colorscheme for everything else.\n", "# We also specifically did not measure that one sample that got a hidden short during modification, since that's also too easy.\n", "group_b = [value for value in data_runs[f'attack-repeat-25-07-09-01-p0.3'] if 'loop_patch' in value['info']['mods'] and value['info']['serial'] != 56]\n", + "group_b = [group_b[i+j] for i in range(1, 71, 10) for j in [1,3,7]]\n", "\n", "print(set(x['info']['specimen'] for x in group_b))\n", "print(len(group_a), group_a[-1]['info']['timestamp'])\n", "print(len(group_b), group_b[-1]['info']['timestamp'])\n", "\n", - "plot_vector_covar(group_a, group_b, plot_cdf=True, fig_out='patch_repeat_tridelta_all_the_data_p0.3', do_lines=True, figsize=(5, 5), smooth=1e-9, triangle_delta='load', batchlabels=batchlabels)\n", - "plot_vector_covar(group_a, group_b, vector_method='min', distance_method='max', plot_cdf=True, fig_out='patch_repeat_tridalta_all_the_data_p0.3_minmax', do_lines=True, figsize=(5, 5), smooth=1e-9, batchlabels=batchlabels)" + "#plot_vector_covar(group_a, group_b, plot_cdf=True, fig_out='patch_repeat_tridelta_all_the_data_p0.3', do_lines=True, figsize=(5, 5), smooth=1e-9, triangle_delta='load', batchlabels=batchlabels)\n", + "plot_vector_covar(group_a, group_b, plot_cdf=True, fig_out='patch_repeat_tridelta_all_the_data_p0.3', do_lines=True, figsize=(3.5,3.5), smooth=None, batchlabels=batchlabels, label_placement='x')\n", + "plot_vector_covar(group_a, group_b, vector_method='min', distance_method='max', plot_cdf=True, fig_out='patch_repeat_tridalta_all_the_data_p0.3_minmax', do_lines=True, figsize=(3.5,3.5), smooth=1e-7, batchlabels=batchlabels, label_placement='x')" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0cdf0b39-47dd-4ab8-81f8-12a83d9b3ef2", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7059f9ac-4c34-438f-8ffb-d1b677c0563d", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/jupyter_notebooks/multi_trace_viewer.ipynb b/jupyter_notebooks/multi_trace_viewer.ipynb index bf7feda..f45a08a 100644 --- a/jupyter_notebooks/multi_trace_viewer.ipynb +++ b/jupyter_notebooks/multi_trace_viewer.ipynb @@ -33,9 +33,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_97648/2846935186.py:45: UserWarning: Unknown pitch for 'cal-open'\n", + "/tmp/ipykernel_11618/2846935186.py:45: UserWarning: Unknown pitch for 'cal-open'\n", " warnings.warn(f'Unknown pitch for {meta!r}')\n", - "/tmp/ipykernel_97648/2846935186.py:45: UserWarning: Unknown pitch for 'baseline-5-open'\n", + "/tmp/ipykernel_11618/2846935186.py:45: UserWarning: Unknown pitch for 'baseline-5-open'\n", " warnings.warn(f'Unknown pitch for {meta!r}')\n" ] } @@ -242,22 +242,26 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 90, "id": "a24d8e51-e083-402b-adde-1154ac1c9544", "metadata": {}, "outputs": [], "source": [ "color_palette = [(181/255, 0.0, 0.0), (1.0, 102/255, 0.0), (0.0, 92/255, 148/255)]\n", "\n", - "def view_trace(trace, xlim=(160, 450), ylim=None, axs=None, select_load=('open', 'load', 'short'), color=None, alpha=None, plot_variance=True, normalize=False, cal=False, smooth=None):\n", + "def view_trace(trace, xlim=(160, 450), ylim=None, axs=None, select_load=('open', 'load', 'short'), color=None, alpha=None, plot_variance=True, normalize=False, cal=False, smooth=None, directions=('fwd', 'flip'), adcs=('adc1', 'adc2'), scale_time=False, yscale=1):\n", " if axs is None:\n", " fig, axs = plt.subplots(2, 2, figsize=(18, 4))\n", " fig.suptitle(trace['info']['specimen'])\n", - " for i, direction in enumerate(['fwd', 'flip']):\n", - " for j, adc in enumerate(['adc1', 'adc2']):\n", + " sampling_period_ns = 1000 / 168 / 32\n", + " for i, direction in enumerate(directions):\n", + " for j, adc in enumerate(adcs):\n", " axs[i, j].grid()\n", " a, b = xlim\n", - " axs[i,j].set_xlim([a, b])\n", + " #if scale_time:\n", + " # axs[i,j].set_xlim([a, b])\n", + " #else:\n", + " # axs[i,j].set_xlim([a*sampling_period_ns, b*sampling_period_ns])\n", " min_y = 1e9\n", " max_y = -1e9\n", " if color:\n", @@ -287,17 +291,23 @@ " \n", " if normalize:\n", " adc_trace -= adc_trace[a:b].mean()\n", - " \n", + " if scale_time:\n", + " t = np.linspace(0, sampling_period_ns*len(adc_trace), len(adc_trace))\n", + " else:\n", + " t = np.linspace(0, len(adc_trace)-1, len(adc_trace))\n", + "\n", + " adc_trace *= yscale\n", + " var_trace *= yscale\n", " min_y = min(min_y, (adc_trace - var_trace)[a:b].min())\n", " max_y = max(max_y, (adc_trace + var_trace)[a:b].max())\n", - " axs[i,j].plot(adc_trace, color=color, alpha=alpha)\n", + " axs[i,j].plot(t, adc_trace, color=color, alpha=alpha, label=f'{direction} {adc}')\n", " if plot_variance:\n", - " axs[i,j].fill_between(adc_trace + var_trace, adc_trace - var_trace, color=color, alpha=0.3*alpha)\n", + " axs[i,j].fill_between(t, adc_trace + var_trace, adc_trace - var_trace, color=color, alpha=0.3*alpha)\n", " dy = max_y - min_y\n", - " if ylim:\n", - " axs[i,j].set_ylim(ylim)\n", - " else:\n", - " axs[i, j].set_ylim([min_y - 0.1*dy, max_y + 0.1*dy])\n", + " #if ylim:\n", + " # axs[i,j].set_ylim(ylim)\n", + " #else:\n", + " # axs[i, j].set_ylim([min_y - 0.1*dy, max_y + 0.1*dy])\n", "\n", "#view_trace(mod_modified[('short_within_trace', 0.3)][0])\n", "#view_trace(mod_modified[('short_within_trace', 0.3)][1])\n", @@ -388,10 +398,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "e760f517-a96d-4295-b9e4-8980df0f4fcf", "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "TypeError", + "evalue": "unsupported operand type(s) for *: 'float' and 'NoneType'", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mTypeError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[7]\u001b[39m\u001b[32m, line 5\u001b[39m\n\u001b[32m 2\u001b[39m group_baseline = \u001b[38;5;28msorted\u001b[39m([value \u001b[38;5;28;01mfor\u001b[39;00m value \u001b[38;5;129;01min\u001b[39;00m entries \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m value[\u001b[33m'\u001b[39m\u001b[33minfo\u001b[39m\u001b[33m'\u001b[39m][\u001b[33m'\u001b[39m\u001b[33mmods\u001b[39m\u001b[33m'\u001b[39m]], key=\u001b[38;5;28;01mlambda\u001b[39;00m value: value[\u001b[33m'\u001b[39m\u001b[33minfo\u001b[39m\u001b[33m'\u001b[39m][\u001b[33m'\u001b[39m\u001b[33mserial\u001b[39m\u001b[33m'\u001b[39m])\n\u001b[32m 3\u001b[39m group_modified = \u001b[38;5;28msorted\u001b[39m([value \u001b[38;5;28;01mfor\u001b[39;00m value \u001b[38;5;129;01min\u001b[39;00m entries \u001b[38;5;28;01mif\u001b[39;00m value[\u001b[33m'\u001b[39m\u001b[33minfo\u001b[39m\u001b[33m'\u001b[39m][\u001b[33m'\u001b[39m\u001b[33mmods\u001b[39m\u001b[33m'\u001b[39m]], key=\u001b[38;5;28;01mlambda\u001b[39;00m value: value[\u001b[33m'\u001b[39m\u001b[33minfo\u001b[39m\u001b[33m'\u001b[39m][\u001b[33m'\u001b[39m\u001b[33mserial\u001b[39m\u001b[33m'\u001b[39m])\n\u001b[32m----> \u001b[39m\u001b[32m5\u001b[39m \u001b[43mview_trace\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgroup_modified\u001b[49m\u001b[43m[\u001b[49m\u001b[32;43m6\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 6\u001b[39m view_trace(group_baseline[\u001b[32m0\u001b[39m])\n", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[6]\u001b[39m\u001b[32m, line 46\u001b[39m, in \u001b[36mview_trace\u001b[39m\u001b[34m(trace, xlim, ylim, axs, select_load, color, alpha, plot_variance, normalize, cal, smooth)\u001b[39m\n\u001b[32m 44\u001b[39m axs[i,j].plot(adc_trace, color=color, alpha=alpha)\n\u001b[32m 45\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m plot_variance:\n\u001b[32m---> \u001b[39m\u001b[32m46\u001b[39m axs[i,j].fill_between(adc_trace + var_trace, adc_trace - var_trace, color=color, alpha=\u001b[32;43m0.3\u001b[39;49m\u001b[43m*\u001b[49m\u001b[43malpha\u001b[49m)\n\u001b[32m 47\u001b[39m dy = max_y - min_y\n\u001b[32m 48\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m ylim:\n", + "\u001b[31mTypeError\u001b[39m: unsupported operand type(s) for *: 'float' and 'NoneType'" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "entries = [value for entry in data.values() for value in entry if value['info']['meta'] == 'interleaved-25-07-02-01-p0.3']\n", "group_baseline = sorted([value for value in entries if not value['info']['mods']], key=lambda value: value['info']['serial'])\n", @@ -423,7 +456,7 @@ "\u001b[31m---------------------------------------------------------------------------\u001b[39m", "\u001b[31mTypeError\u001b[39m Traceback (most recent call last)", "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[8]\u001b[39m\u001b[32m, line 6\u001b[39m\n\u001b[32m 4\u001b[39m \u001b[38;5;28mprint\u001b[39m(group_b[\u001b[32m0\u001b[39m][\u001b[33m'\u001b[39m\u001b[33minfo\u001b[39m\u001b[33m'\u001b[39m])\n\u001b[32m 5\u001b[39m fig, axs = plt.subplots(\u001b[32m2\u001b[39m, \u001b[32m2\u001b[39m, figsize=(\u001b[32m18\u001b[39m, \u001b[32m4\u001b[39m))\n\u001b[32m----> \u001b[39m\u001b[32m6\u001b[39m \u001b[43mview_trace\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgroup_a\u001b[49m\u001b[43m[\u001b[49m\u001b[32;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxs\u001b[49m\u001b[43m=\u001b[49m\u001b[43maxs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 7\u001b[39m view_trace(group_b[\u001b[32m0\u001b[39m], axs=axs)\n\u001b[32m 8\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m ax \u001b[38;5;129;01min\u001b[39;00m axs.flatten():\n", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[4]\u001b[39m\u001b[32m, line 46\u001b[39m, in \u001b[36mview_trace\u001b[39m\u001b[34m(trace, xlim, ylim, axs, select_load, color, alpha, plot_variance, normalize, cal, smooth)\u001b[39m\n\u001b[32m 44\u001b[39m axs[i,j].plot(adc_trace, color=color, alpha=alpha)\n\u001b[32m 45\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m plot_variance:\n\u001b[32m---> \u001b[39m\u001b[32m46\u001b[39m axs[i,j].fill_between(adc_trace + var_trace, adc_trace - var_trace, color=color, alpha=\u001b[32;43m0.3\u001b[39;49m\u001b[43m*\u001b[49m\u001b[43malpha\u001b[49m)\n\u001b[32m 47\u001b[39m dy = max_y - min_y\n\u001b[32m 48\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m ylim:\n", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[6]\u001b[39m\u001b[32m, line 46\u001b[39m, in \u001b[36mview_trace\u001b[39m\u001b[34m(trace, xlim, ylim, axs, select_load, color, alpha, plot_variance, normalize, cal, smooth)\u001b[39m\n\u001b[32m 44\u001b[39m axs[i,j].plot(adc_trace, color=color, alpha=alpha)\n\u001b[32m 45\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m plot_variance:\n\u001b[32m---> \u001b[39m\u001b[32m46\u001b[39m axs[i,j].fill_between(adc_trace + var_trace, adc_trace - var_trace, color=color, alpha=\u001b[32;43m0.3\u001b[39;49m\u001b[43m*\u001b[49m\u001b[43malpha\u001b[49m)\n\u001b[32m 47\u001b[39m dy = max_y - min_y\n\u001b[32m 48\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m ylim:\n", "\u001b[31mTypeError\u001b[39m: unsupported operand type(s) for *: 'float' and 'NoneType'" ] }, @@ -452,7 +485,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 9, "id": "79c081ec-a580-4480-8df5-8ada8a585df3", "metadata": {}, "outputs": [ @@ -494,6 +527,49 @@ " #ax.set_ylim([0.4, 0.5])\n", " pass" ] + }, + { + "cell_type": "code", + "execution_count": 94, + "id": "3a7c2a3b-7fe3-4f08-917e-698309e8c585", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "group_a1 = [value for value in data_runs[f'apply-patch-interleave-reference-25-07-11-03-p0.3'] if value['info']['serial'] != 74]\n", + "group_b1 = [value for value in data_runs[f'apply-patch-interleave-reference-25-07-11-03-p0.3'] if value['info']['serial'] == 74]\n", + "group_a2 = [value for value in data_runs[f'apply-patch-interleave-modified-25-07-11-03-p0.3'] if value['info']['serial'] != 74]\n", + "group_b2 = [value for value in data_runs[f'apply-patch-interleave-modified-25-07-11-03-p0.3'] if value['info']['serial'] == 74]\n", + "\n", + "fig, axs = plt.subplots(1, 2, figsize=(6, 2), sharey=True)\n", + "axs = axs.reshape((1, 2))\n", + "\n", + "for i, trace in enumerate([x for t in zip(group_b1, group_b2) for x in t]):\n", + " view_trace(trace, axs=axs, color=('red' if i%2 else 'black'), alpha=0.3, plot_variance=False, normalize=True, cal=False, xlim=(160, 400), select_load='open', smooth=1e-9, directions=('flip',), scale_time=True, yscale=1e3)\n", + "\n", + "sampling_period_ns = 1000 / 168 / 32\n", + "for ax in axs.flatten():\n", + " ax.set_xlim([180*sampling_period_ns, 250*sampling_period_ns])\n", + " ax.grid()\n", + " ax.set_xlabel(r'$t\\;[\\text{ns}]$')\n", + "axs[0,0].set_ylim([-2.5, 2.5])\n", + "axs[0,0].set_ylabel(r'$V\\;[\\text{mV}]$')\n", + "\n", + "handles, _labels = axs[0,0].get_legend_handles_labels()\n", + "fig.subplots_adjust(top=0.8, bottom=0.2)\n", + "fig.legend(handles[:2], ['Before attack', 'After attack'], loc='upper center', ncol=4, bbox_to_anchor=[0.5, 0.95], frameon=False)\n", + "fig.savefig('../paper/fig_drill_mod_shape_new.pdf')" + ] } ], "metadata": { diff --git a/paper/fig_cdf_open_p0.3.pdf b/paper/fig_cdf_open_p0.3.pdf index aa7575f..b864ce8 100644 Binary files a/paper/fig_cdf_open_p0.3.pdf and b/paper/fig_cdf_open_p0.3.pdf differ diff --git a/paper/fig_cdf_open_p0.3_minmax.pdf b/paper/fig_cdf_open_p0.3_minmax.pdf new file mode 100644 index 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b/paper/fig_covar_short_within_0.3_min_max.pdf index 0927183..0c01085 100644 Binary files a/paper/fig_covar_short_within_0.3_min_max.pdf and b/paper/fig_covar_short_within_0.3_min_max.pdf differ diff --git a/paper/fig_drill_mod_shape_new.pdf b/paper/fig_drill_mod_shape_new.pdf new file mode 100644 index 0000000..90c9529 Binary files /dev/null and b/paper/fig_drill_mod_shape_new.pdf differ diff --git a/paper/fig_patch_interleave_scatter.pdf b/paper/fig_patch_interleave_scatter.pdf index 476d965..1dcff26 100644 Binary files a/paper/fig_patch_interleave_scatter.pdf and b/paper/fig_patch_interleave_scatter.pdf differ diff --git a/paper/fig_results_open_p0.3.txt b/paper/fig_results_open_p0.3.txt index 56fa2aa..b18bf07 100644 --- a/paper/fig_results_open_p0.3.txt +++ b/paper/fig_results_open_p0.3.txt @@ -1,4 +1,4 @@ -Results calculated from plots fig_covar_open_p0.3.pdf / fig_cdf_open_p0.3.pdf on 2025-07-10T16:50:18.765540 +Results calculated from plots fig_covar_open_p0.3.pdf / fig_cdf_open_p0.3.pdf on 2025-07-14T19:47:21.148805 setting threshold for quantile 0.001 Baseline threshold set at 0.976378 diff --git a/paper/fig_results_open_p0.3_minmax.txt b/paper/fig_results_open_p0.3_minmax.txt new file mode 100644 index 0000000..b1d23ac --- /dev/null +++ b/paper/fig_results_open_p0.3_minmax.txt @@ -0,0 +1,12 @@ +Results calculated from plots fig_covar_open_p0.3_minmax.pdf / fig_cdf_open_p0.3_minmax.pdf on 2025-07-14T19:47:21.256754 + +setting threshold for quantile 0.001 +Baseline threshold set at 0.222156 + +Distribution parameters: +Within class: 0.434±0.0686 min: 0.312 max: 0.558 +Cross class: -2.66±0.435 min: -3.46 max: -1.73 + +Type 1 error (false alarm rate): 0.001000000000 +Type 2 error (missed alarm rate): 0.000000000017 +EER: 0.0 th: 0.3114283518396177 \ No newline at end of file diff --git a/paper/fig_results_open_p0.4.txt b/paper/fig_results_open_p0.4.txt index 2b07cef..45c9f8f 100644 --- a/paper/fig_results_open_p0.4.txt +++ b/paper/fig_results_open_p0.4.txt @@ -1,4 +1,4 @@ -Results calculated from plots fig_covar_open_p0.4.pdf / fig_cdf_open_p0.4.pdf on 2025-07-10T16:50:19.105908 +Results calculated from plots fig_covar_open_p0.4.pdf / fig_cdf_open_p0.4.pdf on 2025-07-14T19:47:21.356528 setting threshold for quantile 0.001 Baseline threshold set at 0.961962 diff --git a/paper/fig_results_open_p0.4_minmax.txt b/paper/fig_results_open_p0.4_minmax.txt new file mode 100644 index 0000000..b8fc30e --- /dev/null +++ b/paper/fig_results_open_p0.4_minmax.txt @@ -0,0 +1,12 @@ +Results calculated from plots fig_covar_open_p0.4_minmax.pdf / fig_cdf_open_p0.4_minmax.pdf on 2025-07-14T19:47:21.469098 + +setting threshold for quantile 0.001 +Baseline threshold set at -0.044841 + +Distribution parameters: +Within class: 0.263±0.0995 min: -0.0456 max: 0.438 +Cross class: -1.62±0.282 min: -2.38 max: -1.15 + +Type 1 error (false alarm rate): 0.001000000000 +Type 2 error (missed alarm rate): 0.000000011687 +EER: 0.0 th: -0.05437290229507008 \ No newline at end of file diff --git a/paper/fig_results_patch_interleave_baseline.txt b/paper/fig_results_patch_interleave_baseline.txt index e2a5de7..aa0e9f9 100644 --- a/paper/fig_results_patch_interleave_baseline.txt +++ b/paper/fig_results_patch_interleave_baseline.txt @@ -1,4 +1,4 @@ -Results calculated from plots fig_covar_patch_interleave_baseline.pdf / fig_cdf_patch_interleave_baseline.pdf on 2025-07-11T19:17:04.741667 +Results calculated from plots fig_covar_patch_interleave_baseline.pdf / fig_cdf_patch_interleave_baseline.pdf on 2025-07-14T18:27:54.440965 setting threshold for quantile 0.001 Baseline threshold set at 0.985280 diff --git a/paper/fig_results_patch_interleave_classifier.txt b/paper/fig_results_patch_interleave_classifier.txt new file mode 100644 index 0000000..8d5da74 --- /dev/null +++ b/paper/fig_results_patch_interleave_classifier.txt @@ -0,0 +1,12 @@ +Results calculated from plots fig_covar_patch_interleave_classifier.pdf / fig_cdf_patch_interleave_classifier.pdf on 2025-07-14T18:27:58.660200 + +setting threshold for quantile 0.001 +Baseline threshold set at 0.990168 + +Distribution parameters: +Within class: 0.992±0.000474 min: 0.991 max: 0.993 +Cross class: 0.99±0.000725 min: 0.988 max: 0.991 + +Type 1 error (false alarm rate): 0.001000000000 +Type 2 error (missed alarm rate): 0.212168000058 +EER: 0.0463768115942029 th: 0.9908329311778704 \ No newline at end of file diff --git a/paper/fig_results_patch_ref_exp_interleave_direct.txt b/paper/fig_results_patch_ref_exp_interleave_direct.txt index efdae57..05aa185 100644 --- a/paper/fig_results_patch_ref_exp_interleave_direct.txt +++ b/paper/fig_results_patch_ref_exp_interleave_direct.txt @@ -1,11 +1,11 @@ -Results calculated from plots fig_covar_patch_ref_exp_interleave_direct.pdf / on 2025-07-11T19:17:12.277084 +Results calculated from plots fig_covar_patch_ref_exp_interleave_direct.pdf / on 2025-07-14T19:35:49.298155 setting threshold for quantile 0.001 -Baseline threshold set at 0.979357 +Baseline threshold set at 0.988544 Distribution parameters: -Within class: 0.989±0.00314 min: 0.983 max: 0.993 -Cross class: 0.985±0.00315 min: 0.98 max: 0.991 +Within class: 0.994±0.00162 min: 0.989 max: 0.996 +Cross class: 0.991±0.00145 min: 0.987 max: 0.994 Type 1 error (false alarm rate): 0.001000000000 -Type 2 error (missed alarm rate): 0.972331318110 \ No newline at end of file +Type 2 error (missed alarm rate): 0.959533969920 \ No newline at end of file diff --git a/paper/fig_results_patch_repeat_p0.3.txt b/paper/fig_results_patch_repeat_p0.3.txt index 841ebd8..e5346c4 100644 --- a/paper/fig_results_patch_repeat_p0.3.txt +++ b/paper/fig_results_patch_repeat_p0.3.txt @@ -1,4 +1,4 @@ -Results calculated from plots fig_covar_patch_repeat_p0.3.pdf / fig_cdf_patch_repeat_p0.3.pdf on 2025-07-10T11:36:43.200821 +Results calculated from plots fig_covar_patch_repeat_p0.3.pdf / fig_cdf_patch_repeat_p0.3.pdf on 2025-07-14T21:20:17.911047 setting threshold for quantile 0.001 Baseline threshold set at 0.991727 diff --git a/paper/fig_results_patch_repeat_p0.3_minmax.txt b/paper/fig_results_patch_repeat_p0.3_minmax.txt index 94bd7ec..1fbd81f 100644 --- a/paper/fig_results_patch_repeat_p0.3_minmax.txt +++ b/paper/fig_results_patch_repeat_p0.3_minmax.txt @@ -1,4 +1,4 @@ -Results calculated from plots fig_covar_patch_repeat_p0.3_minmax.pdf / fig_cdf_patch_repeat_p0.3_minmax.pdf on 2025-07-10T11:36:50.914331 +Results calculated from plots fig_covar_patch_repeat_p0.3_minmax.pdf / fig_cdf_patch_repeat_p0.3_minmax.pdf on 2025-07-14T21:20:24.419632 setting threshold for quantile 0.001 Baseline threshold set at 0.470057 diff --git a/paper/fig_results_patch_repeat_tridalta_all_the_data_p0.3_minmax.txt b/paper/fig_results_patch_repeat_tridalta_all_the_data_p0.3_minmax.txt index cc8a7f7..22fc8dc 100644 --- a/paper/fig_results_patch_repeat_tridalta_all_the_data_p0.3_minmax.txt +++ b/paper/fig_results_patch_repeat_tridalta_all_the_data_p0.3_minmax.txt @@ -1,12 +1,12 @@ -Results calculated from plots fig_covar_patch_repeat_tridalta_all_the_data_p0.3_minmax.pdf / fig_cdf_patch_repeat_tridalta_all_the_data_p0.3_minmax.pdf on 2025-07-10T15:58:36.343711 +Results calculated from plots fig_covar_patch_repeat_tridalta_all_the_data_p0.3_minmax.pdf / fig_cdf_patch_repeat_tridalta_all_the_data_p0.3_minmax.pdf on 2025-07-14T21:07:58.608116 setting threshold for quantile 0.001 -Baseline threshold set at 0.328759 +Baseline threshold set at 0.525644 Distribution parameters: -Within class: 0.593±0.0855 min: 0.231 max: 0.77 -Cross class: 0.46±0.0965 min: -0.063 max: 0.711 +Within class: 0.745±0.0709 min: 0.443 max: 0.885 +Cross class: 0.567±0.118 min: 0.155 max: 0.805 Type 1 error (false alarm rate): 0.001000000000 -Type 2 error (missed alarm rate): 0.913000208571 -EER: 0.22598211731369175 th: 0.53232711268001 \ No newline at end of file +Type 2 error (missed alarm rate): 0.635338095839 +EER: 0.16773742265267688 th: 0.6806741308858419 \ No newline at end of file diff --git a/paper/fig_results_patch_repeat_tridelta_all_the_data_p0.3.txt b/paper/fig_results_patch_repeat_tridelta_all_the_data_p0.3.txt index df79ef6..428d065 100644 --- a/paper/fig_results_patch_repeat_tridelta_all_the_data_p0.3.txt +++ b/paper/fig_results_patch_repeat_tridelta_all_the_data_p0.3.txt @@ -1,12 +1,12 @@ -Results calculated from plots fig_covar_patch_repeat_tridelta_all_the_data_p0.3.pdf / fig_cdf_patch_repeat_tridelta_all_the_data_p0.3.pdf on 2025-07-10T15:58:23.897350 +Results calculated from plots fig_covar_patch_repeat_tridelta_all_the_data_p0.3.pdf / fig_cdf_patch_repeat_tridelta_all_the_data_p0.3.pdf on 2025-07-14T21:07:52.114674 setting threshold for quantile 0.001 -Baseline threshold set at 0.998952 +Baseline threshold set at 0.978057 Distribution parameters: -Within class: 0.999±0.000111 min: 0.999 max: 1 -Cross class: 0.999±0.000444 min: 0.997 max: 0.999 +Within class: 0.984±0.00193 min: 0.978 max: 0.989 +Cross class: 0.98±0.00347 min: 0.966 max: 0.986 Type 1 error (false alarm rate): 0.001000000000 -Type 2 error (missed alarm rate): 0.354658531438 -EER: 0.1456372180952635 th: 0.9991936057976704 \ No newline at end of file +Type 2 error (missed alarm rate): 0.690844956542 +EER: 0.19512685790021775 th: 0.9823359725826497 \ No newline at end of file diff --git a/paper/fig_results_probe_points_p0.3.txt b/paper/fig_results_probe_points_p0.3.txt index cd748ed..466c458 100644 --- a/paper/fig_results_probe_points_p0.3.txt +++ b/paper/fig_results_probe_points_p0.3.txt @@ -1,4 +1,4 @@ -Results calculated from plots fig_covar_probe_points_p0.3.pdf / fig_cdf_probe_points_p0.3.pdf on 2025-07-10T16:50:19.460732 +Results calculated from plots fig_covar_probe_points_p0.3.pdf / fig_cdf_probe_points_p0.3.pdf on 2025-07-14T19:47:21.568805 setting threshold for quantile 0.001 Baseline threshold set at 0.976378 diff --git a/paper/fig_results_probe_points_p0.3_minmax.txt b/paper/fig_results_probe_points_p0.3_minmax.txt new file mode 100644 index 0000000..7d6bdcc --- /dev/null +++ b/paper/fig_results_probe_points_p0.3_minmax.txt @@ -0,0 +1,12 @@ +Results calculated from plots fig_covar_probe_points_p0.3_minmax.pdf / fig_cdf_probe_points_p0.3_minmax.pdf on 2025-07-14T19:47:21.673653 + +setting threshold for quantile 0.001 +Baseline threshold set at 0.222156 + +Distribution parameters: +Within class: 0.434±0.0686 min: 0.312 max: 0.558 +Cross class: 0.439±0.0676 min: 0.281 max: 0.563 + +Type 1 error (false alarm rate): 0.001000000000 +Type 2 error (missed alarm rate): 0.999333182506 +EER: 0.5393939393939394 th: 0.4468653841422197 \ No newline at end of file diff --git a/paper/fig_results_probe_points_p0.4.txt b/paper/fig_results_probe_points_p0.4.txt index 9f0d073..d3cc5dd 100644 --- a/paper/fig_results_probe_points_p0.4.txt +++ b/paper/fig_results_probe_points_p0.4.txt @@ -1,4 +1,4 @@ -Results calculated from plots fig_covar_probe_points_p0.4.pdf / fig_cdf_probe_points_p0.4.pdf on 2025-07-10T16:50:19.836853 +Results calculated from plots fig_covar_probe_points_p0.4.pdf / fig_cdf_probe_points_p0.4.pdf on 2025-07-14T19:47:21.777766 setting threshold for quantile 0.001 Baseline threshold set at 0.961962 diff --git a/paper/fig_results_probe_points_p0.4_minmax.txt b/paper/fig_results_probe_points_p0.4_minmax.txt new file mode 100644 index 0000000..67dd6fd --- /dev/null +++ b/paper/fig_results_probe_points_p0.4_minmax.txt @@ -0,0 +1,12 @@ +Results calculated from plots fig_covar_probe_points_p0.4_minmax.pdf / fig_cdf_probe_points_p0.4_minmax.pdf on 2025-07-14T19:47:22.141699 + +setting threshold for quantile 0.001 +Baseline threshold set at -0.044841 + +Distribution parameters: +Within class: 0.263±0.0995 min: -0.0456 max: 0.438 +Cross class: 0.254±0.0908 min: 0.0437 max: 0.397 + +Type 1 error (false alarm rate): 0.001000000000 +Type 2 error (missed alarm rate): 0.999511411088 +EER: 0.4923076923076923 th: 0.2817379300134156 \ No newline at end of file diff --git a/paper/fig_results_short_across_traces_p0.3.txt b/paper/fig_results_short_across_traces_p0.3.txt index dc08cee..cc88c55 100644 --- a/paper/fig_results_short_across_traces_p0.3.txt +++ b/paper/fig_results_short_across_traces_p0.3.txt @@ -1,4 +1,4 @@ -Results calculated from plots fig_covar_short_across_traces_p0.3.pdf / fig_cdf_short_across_traces_p0.3.pdf on 2025-07-10T16:50:17.440748 +Results calculated from plots fig_covar_short_across_traces_p0.3.pdf / fig_cdf_short_across_traces_p0.3.pdf on 2025-07-14T19:47:20.724045 setting threshold for quantile 0.001 Baseline threshold set at 0.976378 diff --git a/paper/fig_results_short_across_traces_p0.3_minmax.txt b/paper/fig_results_short_across_traces_p0.3_minmax.txt new file mode 100644 index 0000000..122293f --- /dev/null +++ b/paper/fig_results_short_across_traces_p0.3_minmax.txt @@ -0,0 +1,12 @@ +Results calculated from plots fig_covar_short_across_traces_p0.3_minmax.pdf / fig_cdf_short_across_traces_p0.3_minmax.pdf on 2025-07-14T19:47:20.833286 + +setting threshold for quantile 0.001 +Baseline threshold set at 0.222156 + +Distribution parameters: +Within class: 0.434±0.0686 min: 0.312 max: 0.558 +Cross class: -4±0.303 min: -4.75 max: -3.5 + +Type 1 error (false alarm rate): 0.001000000000 +Type 2 error (missed alarm rate): 0.000000000000 +EER: 0.0 th: 0.2915482296934595 \ No newline at end of file diff --git a/paper/fig_results_short_across_traces_p0.4.txt b/paper/fig_results_short_across_traces_p0.4.txt index 1b8eaf4..323e6c2 100644 --- a/paper/fig_results_short_across_traces_p0.4.txt +++ b/paper/fig_results_short_across_traces_p0.4.txt @@ -1,4 +1,4 @@ -Results calculated from plots fig_covar_short_across_traces_p0.4.pdf / fig_cdf_short_across_traces_p0.4.pdf on 2025-07-10T16:50:17.721095 +Results calculated from plots fig_covar_short_across_traces_p0.4.pdf / fig_cdf_short_across_traces_p0.4.pdf on 2025-07-14T19:47:20.935964 setting threshold for quantile 0.001 Baseline threshold set at 0.961962 diff --git a/paper/fig_results_short_across_traces_p0.4_minmax.txt b/paper/fig_results_short_across_traces_p0.4_minmax.txt new file mode 100644 index 0000000..3826592 --- /dev/null +++ b/paper/fig_results_short_across_traces_p0.4_minmax.txt @@ -0,0 +1,12 @@ +Results calculated from plots fig_covar_short_across_traces_p0.4_minmax.pdf / fig_cdf_short_across_traces_p0.4_minmax.pdf on 2025-07-14T19:47:21.044313 + +setting threshold for quantile 0.001 +Baseline threshold set at -0.044841 + +Distribution parameters: +Within class: 0.263±0.0995 min: -0.0456 max: 0.438 +Cross class: -2.6±0.907 min: -4.33 max: -1.5 + +Type 1 error (false alarm rate): 0.001000000000 +Type 2 error (missed alarm rate): 0.002394140016 +EER: 0.0 th: -0.08129093490444816 \ No newline at end of file diff --git a/paper/fig_results_short_ref_exp_interleave_direct.txt b/paper/fig_results_short_ref_exp_interleave_direct.txt new file mode 100644 index 0000000..690f23b --- /dev/null +++ b/paper/fig_results_short_ref_exp_interleave_direct.txt @@ -0,0 +1,11 @@ +Results calculated from plots fig_covar_short_ref_exp_interleave_direct.pdf / on 2025-07-11T19:21:56.481574 + +setting threshold for quantile 0.001 +Baseline threshold set at 0.983926 + +Distribution parameters: +Within class: 0.99±0.00183 min: 0.986 max: 0.993 +Cross class: 0.989±0.00152 min: 0.986 max: 0.992 + +Type 1 error (false alarm rate): 0.001000000000 +Type 2 error (missed alarm rate): 0.999767597486 \ No newline at end of file diff --git a/paper/fig_results_short_within_0.3.txt b/paper/fig_results_short_within_0.3.txt new file mode 100644 index 0000000..9ee11ea --- /dev/null +++ b/paper/fig_results_short_within_0.3.txt @@ -0,0 +1,12 @@ +Results calculated from plots fig_covar_short_within_0.3.pdf / fig_cdf_short_within_0.3.pdf on 2025-07-14T19:58:37.881770 + +setting threshold for quantile 0.001 +Baseline threshold set at 0.991740 + +Distribution parameters: +Within class: 0.995±0.00115 min: 0.991 max: 0.997 +Cross class: 0.926±0.0893 min: 0.783 max: 0.996 + +Type 1 error (false alarm rate): 0.001000000000 +Type 2 error (missed alarm rate): 0.231220998491 +EER: 0.16842105263157894 th: 0.9943917901819603 \ No newline at end of file diff --git a/paper/fig_results_short_within_0.3_min_max.txt b/paper/fig_results_short_within_0.3_min_max.txt index 42e7bd2..3d00598 100644 --- a/paper/fig_results_short_within_0.3_min_max.txt +++ b/paper/fig_results_short_within_0.3_min_max.txt @@ -1,4 +1,4 @@ -Results calculated from plots fig_covar_short_within_0.3_min_max.pdf / fig_cdf_short_within_0.3_min_max.pdf on 2025-07-10T17:11:57.629626 +Results calculated from plots fig_covar_short_within_0.3_min_max.pdf / fig_cdf_short_within_0.3_min_max.pdf on 2025-07-14T19:58:37.994713 setting threshold for quantile 0.001 Baseline threshold set at 0.447921 diff --git a/paper/fig_short_interleave_scatter.pdf b/paper/fig_short_interleave_scatter.pdf new file mode 100644 index 0000000..0d2698e Binary files /dev/null and b/paper/fig_short_interleave_scatter.pdf differ diff --git a/paper/paper.bib b/paper/paper.bib index c530cd0..aad03f5 100644 --- a/paper/paper.bib +++ b/paper/paper.bib @@ -10,6 +10,42 @@ pagetotal = {1} } +@article{abelsonKeysDoormats2015, + title = {Keys under Doormats}, + author = {Abelson, Harold and Anderson, Ross and Bellovin, Steven M. and Benaloh, Josh and Blaze, Matt and Diffie, Whitfield "Whit" and Gilmore, John and Green, Matthew and Landau, Susan and Neumann, Peter G. and Rivest, Ronald L. and Schiller, Jeffrey I. and Schneier, Bruce and Specter, Michael A. and Weitzner, Daniel J.}, + date = {2015-09-28}, + journaltitle = {Commun. ACM}, + volume = {58}, + number = {10}, + pages = {24--26}, + issn = {0001-0782}, + doi = {10.1145/2814825}, + url = {https://dl.acm.org/doi/10.1145/2814825}, + urldate = {2025-05-26}, + abstract = {Mandating insecurity by requiring government access to all data and communications.}, + file = {/home/jaseg/Sync/Research/Zotero/2015_Abelson et al_Keys under doormats.pdf} +} + +@article{abelsonRisksKeyRecovery1997, + title = {The Risks of Key Recovery, Key Escrow, and Trusted Third-Party Encryption}, + author = {Abelson, Hal and Anderson, Ross and Bellovin, Steven M. and Benalob, Josh and Blaze, Matt and Diffie, Whitfield and Gilmore, John and Neumann, Peter G. and Rivest, Ronald L. and Schiller, Jeffrey I. and Schneier, Bruce}, + date = {1997-06-01}, + journaltitle = {World Wide Web J.}, + volume = {2}, + number = {3}, + pages = {241--257}, + issn = {1085-2301} +} + +@report{adc2019, + title = {Choose the Right Accelerometer for Predictive Maintenance}, + author = {Campagnie, Bertrand}, + date = {2019}, + institution = {Analog Devices}, + url = {https://www.analog.com/media/en/technical-documentation/tech-articles/Choose-the-Right-Accelerometer-for-Predictive-Maintenance.pdf}, + urldate = {2021-04-01} +} + @online{adhikariDonLookUbiquitous2022, title = {Don't {{Look Up}}: {{Ubiquitous Data Exfiltration Pathways}} in {{Commercial Spaces}}}, shorttitle = {Don't {{Look Up}}}, @@ -47,6 +83,18 @@ file = {/home/jaseg/Sync/Research/Zotero/2019_Agnesi et al_All-fiber self-compensating polarization encoder for quantum key distribution.pdf} } +@article{albartus2020, + title = {{{DANA}} Universal Dataflow Analysis for Gate-Level Netlist Reverse Engineering}, + author = {Albartus, Nils and Hoffmann, Max and Temme, Sebastian and Azriel, Leonid and Paar, Christof}, + date = {2020}, + journaltitle = {IACR Transactions on Cryptographic Hardware and Embedded Systems}, + volume = {2020}, + number = {4}, + pages = {309--336}, + doi = {10.13154/tches.v2020.i4.309-336}, + bibsource = {dblp computer science bibliography, https://dblp.org} +} + @article{albertiniHowAbuseFix, title = {How to {{Abuse}} and {{Fix Authenticated Encryption Without Key Commitment}}}, author = {Albertini, Ange and Duong, Thai and Gueron, Shay and Kölbl, Stefan and Luykx, Atul and Schmieg, Sophie}, @@ -151,6 +199,14 @@ file = {/home/jaseg/Sync/Research/Zotero/2020_Amitonova et al_Quantum key establishment via a multimode fiber.pdf} } +@www{anandtech2015, + title = {Top Tier {{CPU}} Air Coolers {{Q3}} 2015: 9-Way Roundup Review}, + author = {Fylladitakis, Emmanouil D.}, + publisher = {AnandTech}, + url = {https://www.anandtech.com/show/9415/top-tier-cpu-air-coolers-9way-roundup-review/12}, + urldate = {2021-07-08} +} + @inproceedings{anderson1996tamper, title = {Tamper Resistance-a Cautionary Note}, booktitle = {Proceedings of the Second {{Usenix}} Workshop on Electronic Commerce}, @@ -160,6 +216,13 @@ pages = {1--11} } +@book{anderson2020, + title = {Security Engineering}, + author = {Anderson, Ross}, + date = {2020-09-16}, + isbn = {978-1-119-64281-7} +} + @article{andersonCryptographicProcessorsASurvey2006, title = {Cryptographic {{Processors-A Survey}}}, author = {Anderson, R. and Bond, M. and Clulow, J. and Skorobogatov, S.}, @@ -373,6 +436,19 @@ file = {/home/jaseg/Sync/Research/Zotero/Barooti et al_2023_Public-Key Encryption with Quantum Keys.pdf} } +@online{barrettUSSuspectsHackers2015, + title = {U.{{S}}. {{Suspects Hackers}} in {{China Breached About}} 4 {{Million People}}’s {{Records}}, {{Officials Say}}}, + author = {Barrett, Devlin and Yadron, Danny and Paletta, Damian}, + date = {2015-06-04T21:04:00Z}, + url = {http://www.wsj.com/articles/u-s-suspects-hackers-in-china-behind-government-data-breach-sources-say-1433451888}, + urldate = {2025-05-15}, + abstract = {The Federal Bureau of Investigation is probing an apparently far-reaching penetration of data held by the Office of Personnel Management, in which the records of approximately four million individuals were compromised.}, + langid = {american}, + organization = {Wall Street Journal}, + keywords = {Asia,Asia Pacific,BRICS Countries,C&E Executive News Filter,China,Content Types,courts,crime,Crime/Courts,cybercrime,Cybercrime/Hacking,Developing Economies,Eastern Asia,Emerging Market Countries,Factiva Filters,general news,Greater China,hacking,North America,OASN,OCHN,political,Political/General News,SYND,United States,US News}, + file = {/home/jaseg/Zotero/storage/86GYMVME/u-s-suspects-hackers-in-china-behind-government-data-breach-sources-say-1433451888.html} +} + @online{bartusekCryptographyCertifiedDeletion2023, title = {Cryptography with {{Certified Deletion}}}, author = {Bartusek, James and Khurana, Dakshita}, @@ -426,6 +502,14 @@ file = {/home/jaseg/Zotero/storage/BDZCDH85/Baum et al. - 2022 - Moz$$mathbb Z _ 2^k $$arella Efficient Vector-O.pdf} } +@book{beards1996, + title = {Structural Vibration: {{Analysis}} and Damping}, + author = {Beards, C. F.}, + date = {1996}, + publisher = {Wiley}, + isbn = {0-340-64580-6} +} + @inproceedings{beckFuzzyMessageDetection2021, title = {Fuzzy {{Message Detection}}}, booktitle = {Proceedings of the 2021 {{ACM SIGSAC Conference}} on {{Computer}} and {{Communications Security}}}, @@ -588,6 +672,18 @@ langid = {english} } +@book{blechmanTechnologyLimitationInternational1989, + title = {Technology and the Limitation of International Conflict}, + editor = {Blechman, Barry M.}, + date = {1989}, + series = {{{FPI}} Papers in International Affairs}, + publisher = {Foreign Policy Inst. [u.a.]}, + location = {Washington, DC}, + isbn = {978-0-941700-42-9 978-0-941700-43-6}, + langid = {english}, + pagetotal = {185} +} + @inproceedings{blockAutonomicPermissionlessAndroid2017, title = {An Autonomic and Permissionless {{Android}} Covert Channel}, booktitle = {Proceedings of the 10th {{ACM Conference}} on {{Security}} and {{Privacy}} in {{Wireless}} and {{Mobile Networks}}}, @@ -604,6 +700,16 @@ isbn = {978-1-4503-5084-6} } +@misc{boak1973, + title = {A History of {{U}}.{{S}}. Communications Security, Volumes {{I}} and {{II}}}, + author = {Boak, David G.}, + date = {1973}, + url = {https://www.governmentattic.org/18docs/Hist_US_COMSEC_Boak_NSA_1973u.pdf}, + urldate = {2021-09-24}, + howpublished = {Lecture Notes}, + organization = {US National Security Agency (NSA)} +} + @incollection{boyleEfficientPseudorandomCorrelation2019, title = {Efficient {{Pseudorandom Correlation Generators}}: {{Silent OT Extension}} and {{More}}}, shorttitle = {Efficient {{Pseudorandom Correlation Generators}}}, @@ -893,6 +999,17 @@ file = {/home/jaseg/Sync/Research/Zotero/2016_Carrara_Adams_Out-of-Band Covert Channels—A Survey.pdf} } +@book{carterManagingNuclearOperations1987, + title = {Managing Nuclear Operations}, + editor = {Carter, Ashton and Steinbruner, John D. and Zraket, Charles A. and {Brookings Institution} and {Harvard University}}, + date = {1987}, + publisher = {Brookings Institution}, + location = {Washington, D.C}, + isbn = {978-0-8157-1313-5 978-0-8157-1314-2}, + langid = {english}, + pagetotal = {751} +} + @incollection{castryckEfficientKeyRecovery2023, title = {An {{Efficient Key Recovery Attack}} on {{SIDH}}}, booktitle = {Advances in {{Cryptology}} – {{EUROCRYPT}} 2023}, @@ -1095,6 +1212,18 @@ file = {/home/jaseg/Zotero/storage/RKFV7HX5/Choudhuri et al. - 2021 - Fluid MPC Secure Multiparty Computation with Dyna.pdf} } +@inreference{ChubbDetectorLock2025, + title = {Chubb Detector Lock}, + booktitle = {Wikipedia}, + date = {2025-01-05T23:12:12Z}, + url = {https://en.wikipedia.org/w/index.php?title=Chubb_detector_lock&oldid=1267621709}, + urldate = {2025-04-17}, + abstract = {A Chubb detector lock is a lever tumbler lock with an integral security feature, a re-locking device, which frustrates unauthorised access attempts and indicates to the lock's owner that it has been interfered with. When someone tries to pick the lock or to open it using the wrong key, the lock is designed to jam in a locked state until (depending on the lock) either a special regulator key or the original key is inserted and turned in a different direction. This alerts the owner to the fact that the lock has been tampered with. Any person who attempts to pick a detector lock must avoid triggering the automatic jamming mechanism. If the automatic jamming mechanism is accidentally triggered (which happens when any one of the levers is lifted too high) the lock-picker has the additional problem of resetting the detector mechanism before the next attempt to open the lock. This introduces additional complexity into the task, increasing the degree of lock-picking skill required to a level which few people have. The first detector lock was produced in 1818 by Jeremiah Chubb of Portsmouth, England, as the result of a government competition to create an unpickable lock. It remained unpicked until the Great Exhibition of 1851.}, + langid = {english}, + annotation = {Page Version ID: 1267621709}, + file = {/home/jaseg/Zotero/storage/689DCTN6/Chubb_detector_lock.html} +} + @inproceedings{cifuentesPoorMansHardware2016, title = {Poor {{Man}}'s {{Hardware Security Module}} ({{pmHSM}}): {{A Threshold Cryptographic Backend}} for {{DNSSEC}}}, shorttitle = {Poor {{Man}}'s {{Hardware Security Module}} ({{pmHSM}})}, @@ -1339,6 +1468,14 @@ file = {/home/jaseg/Zotero/storage/8ACFQAKY/de Souza et al. - 2008 - Audit and backup procedures for hardware security .pdf} } +@www{dexter2015, + title = {Shopshifting: {{The}} Potential for Payment System Abuse}, + author = {Nohl, Karsten and Bräunlein, Fabian and {dexter}}, + date = {2015-12-27}, + publisher = {32C3 Chaos Communication Congress}, + url = {https://media.ccc.de/v/32c3-7368-shopshifting#t=2452} +} + @article{diamantiPracticalChallengesQuantum2016, title = {Practical Challenges in Quantum Key Distribution}, author = {Diamanti, Eleni and Lo, Hoi-Kwong and Qi, Bing and Yuan, Zhiliang}, @@ -1394,6 +1531,14 @@ file = {/home/jaseg/Zotero/storage/VE42VHUT/Dittmer et al. - 2022 - Authenticated Garbling from Simple Correlations.pdf} } +@book{dixon2007, + title = {The Shock Absorber Handbook}, + author = {Dixon, John C.}, + date = {2007}, + publisher = {Wiley}, + isbn = {978-0-470-51020-9} +} + @misc{dorseyHighSpeedDataTransmission2010, title = {High-{{Speed Data Transmission}} and {{Rotary Platforms}}: {{Slip Rings}}, {{Fiber Optic Rotary Joints}}, and {{Multiplexers}}}, author = {Dorsey, Glenn}, @@ -1403,6 +1548,16 @@ organization = {Moog, Inc.} } +@inproceedings{drimer2008, + title = {Thinking inside the Box: System-Level Failures of Tamper Proofing}, + booktitle = {2008 {{IEEE}} Symposium on Security and Privacy (Sp 2008)}, + author = {Drimer, Saar and Murdoch, Steven J and Anderson, Ross}, + date = {2008}, + pages = {281--295}, + publisher = {IEEE}, + x-fetchedfrom = {Google Scholar} +} + @incollection{dulekSecureMultipartyQuantum2020, title = {Secure {{Multi-party Quantum Computation}} with a {{Dishonest Majority}}}, author = {Dulek, Yfke and Grilo, Alex B. and Jeffery, Stacey and Majenz, Christian and Schaffner, Christian}, @@ -1479,6 +1634,16 @@ file = {/home/jaseg/Sync/Research/Zotero/Dür et al_2017_Towards a quantum internet.pdf} } +@thesis{e2013, + type = {phdthesis}, + title = {On-Shaft Vibration Measurement Using a {{MEMS}} Accelerometer for Faults Diagnosis in Rotating Machines}, + author = {Elnady, Maged Elsaid}, + date = {2013}, + institution = {University of Manchester}, + url = {https://www.research.manchester.ac.uk/portal/files/54530535/FULL_TEXT.PDF}, + urldate = {2021-04-01} +} + @incollection{eppenAnforderungenEinzelteileRundfunkempfanger1927, title = {Anforderungen an Die {{Einzelteile}} Der {{Rundfunkempfänger}}; {{Gesichtspunkte}} Für Den {{Bau}} Der {{Geräte}}}, booktitle = {Die Wissenschaftlichen {{Grundlagen}} Des {{Rundfunkempfangs}}}, @@ -1526,6 +1691,15 @@ file = {/home/jaseg/Zotero/storage/4EH2UCP5/Evans et al. - A Pragmatic Introduction to Secure Multi-Party Com.pdf} } +@www{faa2018, + title = {Pack Safe: {{Batteries}}, Lithium}, + author = {Administration, US Federal Aviation}, + date = {2018-05-31}, + publisher = {US Federal Aviation Administration}, + url = {https://www.faa.gov/hazmat/packsafe/more_info/?hazmat=7}, + urldate = {2021-07-12} +} + @article{fanSimultaneousWirelessPower2024, title = {A {{Simultaneous Wireless Power}} and {{Coil Inductance Insensitive Data Transfer System}} for {{Rotary Structures}}}, author = {Fan, Yuanshuang and Hu, Hongsheng and Sun, Yue and Hu, Han and Wu, Sihan}, @@ -1582,6 +1756,17 @@ keywords = {twisted-inductors} } +@report{fischlinKryptographischeAnalyseSpezifikation2021, + title = {Kryptographische Analyse Spezifikation Schlüsselgenerierungsdienst ePA}, + author = {Fischlin, Marc}, + date = {2021-12}, + institution = {Technische Universität Darmstadt}, + url = {https://www.gematik.de/media/erezept/SGD_Analyse_2021.pdf}, + urldate = {2025-05-15}, + langid = {german}, + file = {/home/jaseg/Zotero/storage/E6VVYUK5/SGD_Analyse_2021.pdf} +} + @book{flemingPrinciplesElectricWave1910, title = {The {{Principles}} of {{Electric Wave Telegraphy}} and {{Telephony}}}, author = {Fleming, J. A.}, @@ -1591,6 +1776,26 @@ keywords = {twisted-inductor} } +@online{fraunhofersitAbschlussberichtSicherheitsanalyseGesamtsystems2024, + title = {Abschlussbericht {{Sicherheitsanalyse}} Des {{Gesamtsystems ePA}} Für Alle}, + author = {{Fraunhofer SIT}}, + date = {2024-08-09}, + url = {https://www.sit.fraunhofer.de/fileadmin/dokumente/studien_und_technical_reports/Abschlussbericht_Sicherheitsanalyse_ePA_fuer_alle_Fraunhofer_SIT.pdf}, + urldate = {2025-05-16}, + file = {/home/jaseg/Zotero/storage/AD5MS92X/Abschlussbericht_Sicherheitsanalyse_ePA_fuer_alle_Fraunhofer_SIT.pdf} +} + +@article{frazelle2019, + title = {Securing the {{Boot Process}}: {{The}} Hardware Root of Trust}, + author = {Frazelle, Jessie}, + date = {2019-12-01}, + journaltitle = {ACM queue : tomorrow's computing today}, + shortjournal = {ACM Queue}, + doi = {10.1145/3380774.3382016}, + url = {https://dl.acm.org/doi/fullHtml/10.1145/3380774.3382016}, + urldate = {2020-10-22} +} + @online{fs1M12FSC, title = {1M 12F SC/APC Singlemode Farbcodiertes LWL-Pigtail - FS.com Deutschland}, author = {FS}, @@ -1681,6 +1886,44 @@ file = {/home/jaseg/Zotero/storage/68BWJ8CR/Garb et al. - 2022 - The Wiretap Channel for Capacitive PUF-Based Secur.pdf} } +@online{gematikSpezifikationAktensystemEPA2025, + title = {Spezifikation Aktensystem ePA für alle v1.4.1}, + author = {{gematik}}, + date = {2025-05-09}, + url = {https://gemspec.gematik.de/docs/gemSpec/gemSpec_Aktensystem_ePAfueralle/latest/}, + urldate = {2025-05-16}, + langid = {ngerman}, + file = {/home/jaseg/Zotero/storage/7UYIC2N4/latest.html} +} + +@online{gematikSpezifikationSchluesselgenerierungsdienstEPA2023, + title = {Spezifikation Schlüsselgenerierungsdienst ePA v1.6.0}, + author = {{gematik}}, + date = {2023-03-31}, + url = {https://gemspec.gematik.de/downloads/gemSpec/gemSpec_SGD_ePA/gemSpec_SGD_ePA_V1.6.0.pdf}, + urldate = {2025-05-26}, + langid = {ngerman}, + file = {/home/jaseg/Zotero/storage/79DUVAQG/Spezifikation Schlüsselgenerierungsdienst ePA.pdf} +} + +@online{gematikUbergreifendeSpezifikationVerwendung2024, + title = {Übergreifende {{Spezifikation Verwendung}} Kryptographischer {{Algorithmen}} in Der {{Telematikinfrastruktur}} v2.28.1}, + author = {{gematik}}, + date = {2024-02-23}, + url = {https://gemspec.gematik.de/downloads/gemSpec/gemSpec_Krypt/gemSpec_Krypt_V2.28.1.html}, + urldate = {2025-05-16}, + file = {/home/jaseg/Zotero/storage/4G4DKG53/gemSpec_Krypt_V2.28.1.html} +} + +@online{gematikUebergreifendeSpezifikationVerwendung2025, + title = {Übergreifende Spezifikation Verwendung kryptographischer Algorithmen in der Telematikinfrastruktur v2.40.0}, + author = {{gematik}}, + date = {2025-03-28}, + url = {https://gemspec.gematik.de/downloads/gemSpec/gemSpec_Krypt/gemSpec_Krypt_V2.40.0.pdf}, + langid = {ngerman}, + file = {/home/jaseg/Zotero/storage/PTWL3X45/Übergreifende Spezifikation Verwendung kryptograph.pdf} +} + @software{GerbonaraToolsHandle, title = {Gerbonara: {{Tools}} to Handle {{Gerber}} and {{Excellon}} Files in {{Python}}}, shorttitle = {Gerbonara}, @@ -1691,6 +1934,17 @@ file = {/home/jaseg/Zotero/storage/9XQ63WGV/gerbonara.html} } +@inproceedings{german2007, + title = {Event Data Recorders in the Analysis of Frontal Impacts}, + booktitle = {Annual Proceedings of the Association for the Advancement of Automotive Medicine}, + author = {German, A. and Comeau, J-L. and K.J. McClafferty, M.J. Shkrum and Tiessen, P.F.}, + date = {2007}, + number = {51}, + pages = {225--243}, + url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3217513/}, + urldate = {2021-07-12} +} + @article{geuzaineGmsh3DFinite2009, title = {Gmsh: {{A}} 3‐{{D}} Finite Element Mesh Generator with Built‐in Pre‐ and Post‐processing Facilities}, shorttitle = {Gmsh}, @@ -1769,6 +2023,13 @@ file = {/home/jaseg/Zotero/storage/IUACRFKT/Girault et al. - 1988 - A Generalized Birthday Attack.pdf} } +@online{GithubRepositoryERPFD, + title = {Github Repository: {{eRP-FD}}/Vau-Hsm}, + url = {https://github.com/eRP-FD/vau-hsm/tree/master}, + urldate = {2025-05-16}, + file = {/home/jaseg/Zotero/storage/33V8YQTK/master.html} +} + @inproceedings{goldbergPlanarFabricationMesoscale2014, title = {Planar Fabrication of a Mesoscale Voice Coil Actuator}, booktitle = {2014 {{IEEE International Conference}} on {{Robotics}} and {{Automation}} ({{ICRA}})}, @@ -1870,6 +2131,16 @@ file = {/home/jaseg/Sync/Research/Zotero/2022_Götte_Scheuermann_Can’t Touch This.pdf} } +@online{greenbergSignalMoreEncrypted2024, + title = {Signal {{Is More Than Encrypted Messaging}}. {{Under Meredith Whittaker}}, {{It}}’s {{Out}} to {{Prove Surveillance Capitalism Wrong}}}, + author = {Greenberg, Andy}, + date = {2024-08-28}, + url = {https://www.wired.com/story/meredith-whittaker-signal/}, + urldate = {2025-06-13}, + organization = {WIRED Magazine}, + annotation = {Archive URL: https://archive.is/J1ZlG} +} + @inproceedings{griloObliviousTransferMiniQCrypt2021, title = {Oblivious {{Transfer Is}} in {{MiniQCrypt}}}, booktitle = {Advances in {{Cryptology}} – {{EUROCRYPT}} 2021}, @@ -1894,6 +2165,14 @@ file = {/home/jaseg/Zotero/storage/PSGQDYRQ/Grisafi et al. - PISTIS Trusted Computing Architecture for Low-end.pdf} } +@standard{GrobkonzeptEPAFuer2023, + title = {Grobkonzept ePA für alle}, + date = {2023-12-13}, + langid = {ngerman}, + version = {1.0.0}, + file = {/home/jaseg/Zotero/storage/XRXV6BY6/Grobkonzept ePA für alle.pdf} +} + @article{grunenfelderFastSinglephotonDetectors2023, title = {Fast Single-Photon Detectors and Real-Time Key Distillation Enable High Secret-Key-Rate Quantum Key Distribution Systems}, author = {Grünenfelder, Fadri and Boaron, Alberto and Resta, Giovanni V. and Perrenoud, Matthieu and Rusca, Davide and Barreiro, Claudio and Houlmann, Raphaël and Sax, Rebecka and Stasi, Lorenzo and El-Khoury, Sylvain and Hänggi, Esther and Bosshard, Nico and Bussières, Félix and Zbinden, Hugo}, @@ -1930,6 +2209,15 @@ file = {/home/jaseg/Sync/Research/Zotero/Grünenfelder et al_2021_The limits of multiplexing quantum and classical channels.pdf;/home/jaseg/Zotero/storage/R7X3RFMF/40314.html} } +@report{gs21, + title = {Tech Report: {{Inerial}} Hsms Thwart Advanced Physical Attacks}, + author = {{Jan Sebastian Götte and Björn Scheuermann}}, + date = {2021-01-14}, + institution = {Alexander von Humboldt Institut für Internet und Gesellschaft}, + url = {https://eprint.iacr.org/2021/055}, + urldate = {2021-04-13} +} + @article{guazziNoncontactMeasurementOxygen2015, title = {Non-Contact Measurement of Oxygen Saturation with an {{RGB}} Camera}, author = {Guazzi, Alessandro R. and Villarroel, Mauricio and Jorge, João and Daly, Jonathan and Frise, Matthew C. and Robbins, Peter A. and Tarassenko, Lionel}, @@ -1972,6 +2260,24 @@ file = {/home/jaseg/Zotero/storage/LUWQNB8Q/Guri et al. - Fansmitter Acoustic Data Exfiltration from (Speak.pdf} } +@article{guriFansmitterAcousticData2020, + title = {Fansmitter: {{Acoustic}} Data Exfiltration from Air-{{Gapped}} Computers via Fans Noise}, + shorttitle = {Fansmitter}, + author = {Guri, Mordechai and Solewicz, Yosef and Elovici, Yuval}, + date = {2020-04-01}, + journaltitle = {Computers \& Security}, + shortjournal = {Computers \& Security}, + volume = {91}, + pages = {101721}, + issn = {0167-4048}, + doi = {10.1016/j.cose.2020.101721}, + url = {https://www.sciencedirect.com/science/article/pii/S0167404820300080}, + urldate = {2025-05-27}, + abstract = {Computers that contain sensitive information are often maintained in air-gapped isolation. In this defensive measure, a computer is disconnected from the Internet - logically and physically - preventing accidental or intentional leakage of sensitive information outward. In recent years it has been shown that malware can leak data over an air-gap by transmitting sonic and ultrasonic signals from a computer speaker. In order to eliminate such acoustic covert channels, current best practice recommends the elimination of speakers in secured computers, thereby creating a so-called ‘audio-gapped’ system. In this paper, we present ‘Fansmitter,’ a malware that can acoustically exfiltrate data from air-gapped computers, even when audio hardware and speakers are not present. Our method utilizes the noise emitted from the CPU, GPU, and chassis fans. We show that a software can regulate the internal fans’ rotation speed in order to control their acoustic signal, known as blade pass frequency (BPF). Binary data can be modulated and transmitted over these audio signals to a remote microphone (e.g., a nearby smartphone). We present design considerations, including acoustic waveform analysis, data modulation and demodulation, and data transmission and reception. We evaluate the acoustic covert channel with various fans at different distances and present the results. We also discuss issues such as stealth, interference, and countermeasures. Using our method we successfully transmitted data from audio-less, air-gapped computers, to a mobile phone in the same room. We demonstrated an effective transmission at distances of 1–8~m, with a maximum bit rate of 60 bit/min per fan.}, + keywords = {Air gaps,Computer viruses,Network security}, + file = {/home/jaseg/Zotero/storage/G4337H6G/S0167404820300080.html} +} + @inproceedings{guriMOSQUITOCovertUltrasonic2018, title = {{{MOSQUITO}}: {{Covert Ultrasonic Transmissions Between Two Air-Gapped Computers Using Speaker-to-Speaker Communication}}}, shorttitle = {{{MOSQUITO}}}, @@ -2019,6 +2325,15 @@ abstract = {Contact discovery allows users of mobile messengers to conveniently connect with people in their address book. In this work, we demonstrate that severe privacy issues exist in currently deployed contact discovery methods and propose suitable mitigations.Our study of three popular messengers\ (WhatsApp, Signal, and Telegram) shows that large-scale crawling attacks are\ (still) possible. Using an accurate database of mobile phone number prefixes and very few resources, we queried\ 10 \% of\ US mobile phone numbers for\ WhatsApp and\ 100 \% for\ Signal. For\ Telegram, we find that its\ API exposes a wide range of sensitive information, even about numbers not registered with the service. We present interesting\ (cross-messenger) usage statistics, which also reveal that very few users change the default privacy settings.Furthermore, we demonstrate that currently deployed hashing-based contact discovery protocols are severely broken by comparing three methods for efficient hash reversal. Most notably, we show that with the password cracking tool\ “JTR,” we can iterate through the entire worldwide mobile phone number space in\ \< 150 s on a consumer-grade\ GPU. We also propose a significantly improved rainbow table construction for non-uniformly distributed input domains that is of independent interest.Regarding mitigations, we most notably propose two novel rate-limiting schemes: our\ incremental contact discovery for services without server-side contact storage strictly improves over\ Signal’s current approach while being compatible with private set intersection, whereas our\ differential scheme allows even stricter rate limits at the overhead for service providers to store a small constant-size state that does not reveal any contact information.} } +@www{haines2006, + title = {{{US}} Outfit Patents 'invisible' {{UAV}}: {{Stealth}} through Persistence of Vision}, + author = {Haines, Lester}, + editor = {Register, The}, + date = {2006-09-25}, + url = {https://www.theregister.com/2006/09/25/phantom_sentinel/}, + urldate = {2020-09-17} +} + @inproceedings{hanScalingHardwareSecurity2019, title = {Toward Scaling Hardware Security Module for Emerging Cloud Services}, booktitle = {Proceedings of the 4th {{Workshop}} on {{System Software}} for {{Trusted Execution}}}, @@ -2101,6 +2416,22 @@ file = {/home/jaseg/Sync/Research/Zotero/Heath et al_GRAM with O(log2 n) Overhead.pdf} } +@www{heise2020t2jailbreak, + title = {Jailbreaker Nehmen {{T2-sicherheitschip}} von Macs Ins Visier}, + author = {Becker, Leo}, + date = {2020-03-11}, + publisher = {Heise Online / Heise Online}, + url = {https://www.heise.de/mac-and-i/meldung/Jailbreaker-nehmen-T2-Sicherheitschip-von-Macs-ins-Visier-4681131.html} +} + +@www{heise2021ovh, + title = {Cloud-{{Dienstleister OVH}}: {{Feuer}} Zerstört {{Rechenzentrum}}, Ein Weiteres Beschädigt}, + author = {Holland, Martin}, + date = {2021-03-10}, + publisher = {Heise Online / Heise Online}, + url = {https://www.heise.de/news/OVH-Feuer-zerstoert-Rechenzentrum-in-Strassburg-ein-weiteres-beschaedigt-5076320.html} +} + @article{helfinstineOpticalFibreStrength1982, title = {Optical Fibre Strength/Fatigue Experiments}, author = {Helfinstine, J. D. and Quan, F.}, @@ -2142,6 +2473,16 @@ file = {/home/jaseg/Sync/Research/Zotero/2014_Hiemstra_Design of Moving Magnet Actuators for Large-range Flexure-based Nanopositioning.pdf} } +@inproceedings{hinagaThermalEffectsPCB2010, + title = {Thermal {{Effects}} on {{PCB Laminate Material Dielectric Constant}} and {{Dissipation Factor}}}, + author = {Hinaga, Scott and Koledintseva, Marina Y. and Drewniak, James L. and Koul, Amendra and Zhou, Fan}, + date = {2010}, + abstract = {Values for printed circuit board (PCB) laminate dielectric constant (Dk) and dissipation factor (Df) used in circuit design and signal integrity (SI) modeling are typically those presented on laminate maker datasheets. In most cases, these values are derived from measurements on samples which have not been exposed to thermal stresses representative of the printed circuit board (PCB) assembly process. This paper discusses the changes in Dk and Df values for a variety of laminate materials following simulated assembly thermal exposure of test vehicles to six SMT cycles at 260°C (Pb-free) or 225°C (SnPb eutectic). An additional concern arises around an effect of operating temperatures upon the effective Dk and Df of PCB materials. Due to thermal radiation from active IC devices, power supplies, etc., the operating temperature of PCBs within a network equipment chassis is typically higher than the 23-25°C value at which Dk and Df are measured and reported. This paper also describes the changes in Dk and Df observed when the test samples were measured at temperatures of 50°C and 75°C.}, + eventtitle = {{{IPC Apex Expo}}}, + langid = {english}, + file = {/home/jaseg/Zotero/storage/EATYK8AG/Hinaga - Thermal Effects on PCB Laminate Material Dielectri.pdf} +} + @inproceedings{hongDesignCompensationControl2020, title = {Design and {{Compensation Control}} of a {{Flexible Instrument}} for {{Endoscopic Surgery}}}, booktitle = {2020 {{IEEE International Conference}} on {{Robotics}} and {{Automation}} ({{ICRA}})}, @@ -2266,6 +2607,26 @@ file = {/home/jaseg/Sync/Research/Zotero/Huttner et al_2022_Long-range QKD without trusted nodes is not possible with current technology.pdf} } +@book{iaea2011, + title = {Safeguards, Techniques and Equipment}, + author = {{International Atomic Energy Agency}}, + date = {2011}, + series = {International Nuclear Verification Series}, + volume = {1}, + url = {https://www-pub.iaea.org/MTCD/Publications/PDF/nvs1_web.pdf}, + urldate = {2021-04-01}, + isbn = {978-92-0-118910-3} +} + +@www{iana21, + title = {Root Zone {{KSK}} Operator Key Management Procedure}, + author = {{Root Zone KSK Operator Policy Management Authority}}, + date = {2021-09-22}, + url = {https://www.iana.org/dnssec/procedures/ksk-operator/KSK_Key_Management_Procedure_v3.4.pdf}, + urldate = {2021-10-07}, + version = {Version 3.4} +} + @online{IEEEXploreFullText, title = {{{IEEE Xplore Full-Text PDF}}:}, url = {https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=514853}, @@ -2287,6 +2648,26 @@ file = {/home/jaseg/Zotero/storage/HJJK32NF/stamp.html} } +@www{ika2002, + title = {A Test Procedure for Airbags}, + date = {2002}, + series = {{{CITA Research}} Study Programme on {{Electronically}} Controlled Systems on Vehicles}, + publisher = {International Motor Vehicle Inspection Commitee / Rheinisch-Westfälischen Technischen Hochschule (RWTH) Aachen, Institut für Kraftfahrwesen Aachen (IKA)} +} + +@article{immler2019, + title = {Secure Physical Enclosures from Covers with Tamper-Resistance}, + author = {Immler, Vincent and Obermaier, Johannes and Ng, Kuan Kuan and Ke, Fei Xiang and Lee, Jin Yu and Lim, Yak Peng and Oh, Wei Koon and Wee, Keng Hoong and Sigl, Georg}, + date = {2019}, + journaltitle = {IACR transactions on cryptographic hardware and embedded systems.}, + shortjournal = {IACR Transactions on Cryptographic Hardware and Embedded Systems}, + publisher = {IACR}, + issn = {2569-2925}, + doi = {10.13154/tches.v2019.i1.51-96}, + url = {https://tches.iacr.org/index.php/TCHES/article/view/7334/6506}, + urldate = {2020-09-16} +} + @inproceedings{immlerBTREPIDBatterylessTamperresistant2018, title = {B-{{TREPID}}: {{Batteryless}} Tamper-Resistant Envelope with a {{PUF}} and Integrity Detection}, shorttitle = {B-{{TREPID}}}, @@ -2347,6 +2728,34 @@ file = {/home/jaseg/Zotero/storage/K9YRK595/Implementation Security of Quantum Cryptography - .pdf} } +@inproceedings{irikura2012, + title = {High Acceleration Motions Generated from the 2011 Pacific Coast off Tohoku, Japan Earthquake}, + booktitle = {Proceedings of the 15th World Conference on Earthquake Engineering}, + author = {Irikura, K and Kurahashi, S}, + date = {2012}, + pages = {24--28} +} + +@report{isaacs2013, + title = {Tamper Proof, Tamper Evident Encryption Technology}, + author = {Isaacs, Phil and Morris Jr, Thomas and Fisher, Michael J and Cuthbert, Keith}, + date = {2013}, + journaltitle = {Pan pacific microelectronics symposium}, + institution = {Surface Mount Technology Association / Surface Mount Technology Association}, + x-fetchedfrom = {Google Scholar} +} + +@online{ISOIEC19790, + title = {{{ISO}}/{{IEC}} 19790:2025}, + shorttitle = {{{ISO}}/{{IEC}} 19790}, + url = {https://www.iso.org/standard/82423.html}, + urldate = {2025-05-15}, + abstract = {Information security, cybersecurity and privacy protection — Security requirements for cryptographic modules}, + langid = {english}, + organization = {ISO}, + file = {/home/jaseg/Zotero/storage/CVBBSX3N/82423.html} +} + @online{ISOIEC24759, title = {{{ISO}}/{{IEC}} 24759:2025}, shorttitle = {{{ISO}}/{{IEC}} 24759}, @@ -2417,6 +2826,15 @@ langid = {english} } +@inproceedings{johnson2018, + title = {Titan: Enabling a Transparent Silicon Root of Trust for {{Cloud}}}, + booktitle = {Hot Chips: A Symposium on High Performance Chips}, + author = {Johnson, Scott and Rizzo, Dominic and Ranganathan, Parthasarathy and McCune, Jon and Ho, Richard}, + date = {2018}, + url = {https://www.hotchips.org/hc30/1conf/1.14_Google_Titan_GoogleFinalTitanHotChips2018.pdf}, + x-fetchedfrom = {Google Scholar} +} + @online{JUNOSubmarineNetworks, title = {{{JUNO}} - {{Submarine Networks}}}, url = {https://www.submarinenetworks.com/en/systems/trans-pacific/juno}, @@ -2514,6 +2932,16 @@ file = {/home/jaseg/Zotero/storage/M6LSM6ML/Keller et al. - 2017 - Faster Secure Multi-party Computation of AES and D.pdf} } +@book{kelly1993, + title = {Fundamentals of Mechanical Vibrations}, + author = {Kelly, S. Graham}, + date = {1993}, + series = {{{McGraw-hill}} Series in Mechanical Engineering}, + edition = {2}, + publisher = {McGraw-Hill}, + isbn = {0-07-230092-2} +} + @online{KiCadEDA, title = {{{KiCad EDA}}}, url = {https://www.kicad.org/}, @@ -2523,6 +2951,17 @@ file = {/home/jaseg/Zotero/storage/IYJUIHPL/www.kicad.org.html} } +@article{kim2018, + title = {Intelligent Intrusion Detection System Featuring a Virtual Fence, Active Intruder Detection, Classification, Tracking, and Action Recognition}, + author = {Kim, Seung Hyun and Lim, Su Chang and others}, + date = {2018}, + journaltitle = {Annals of Nuclear Energy}, + volume = {112}, + pages = {845--855}, + publisher = {Elsevier}, + x-fetchedfrom = {Google Scholar} +} + @article{kimAdvancementFlexibleRobot2022, title = {Advancement of {{Flexible Robot Technologies}} for {{Endoluminal Surgeries}}}, author = {Kim, Joonhwan and family=Mathelin, given=Michel, prefix=de, useprefix=true and Ikuta, Koji and Kwon, Dong-Soo}, @@ -2610,6 +3049,18 @@ file = {/home/jaseg/Zotero/storage/4NYR9495/Koblah et al. - 2022 - Hardware Moving Target Defenses against Physical A.pdf} } +@online{kochMoreMoreExperts2025, + title = {More and More Experts Warn against Electronic Patient Records}, + author = {Koch, Marie-Claire}, + date = {2025-01-10}, + url = {https://www.heise.de/en/news/More-and-more-experts-warn-against-electronic-patient-records-10235907.html}, + urldate = {2025-05-26}, + abstract = {The electronic patient file is due to be launched in a few days, but more and more experts are advising against it or do not consider it advisable.}, + langid = {english}, + organization = {heise online}, + file = {/home/jaseg/Zotero/storage/XQRRKELL/More-and-more-experts-warn-against-electronic-patient-records-10235907.html} +} + @inproceedings{kodwaniSecurityKeyDerivation2021, title = {On {{Security}} of {{Key Derivation Functions}} in {{Password-based Cryptography}}}, booktitle = {2021 {{IEEE International Conference}} on {{Cyber Security}} and {{Resilience}} ({{CSR}})}, @@ -2685,6 +3136,14 @@ file = {/home/jaseg/Sync/Research/Zotero/Kolesnikov_2005_Gate Evaluation Secret Sharing and Secure One-Round Two-Party Computation.pdf} } +@book{kordyban1998, + title = {Hot Air Rises and Heat Sinks: {{Everything}} You Know about Cooling Electronics Is Wrong}, + author = {Kordyban, Tony}, + date = {1998}, + publisher = {ASME}, + isbn = {978-0-7918-0074-4} +} + @inproceedings{kozlowskiLargeScaleQuantumNetworks2019, title = {Towards {{Large-Scale Quantum Networks}}}, booktitle = {Proceedings of the {{Sixth Annual ACM International Conference}} on {{Nanoscale Computing}} and {{Communication}}}, @@ -2775,6 +3234,14 @@ file = {/home/jaseg/Sync/Research/Zotero/2023_Krachenfels et al_Trojan awakener.pdf} } +@article{kreft2012, + title = {Cocoon-{{PUF}}, a Novel Mechatronic Secure Element Technology}, + author = {Kreft, Heinz and Adi, Wael}, + date = {2012}, + journaltitle = {2012 NASA/ESA Conference on Adaptive Hardware and Systems (AHS)}, + doi = {10.1109/ahs.2012.6268655} +} + @inproceedings{kryjakFPGAImplementationCamera2012, title = {{{FPGA}} Implementation of Camera Tamper Detection in Real-Time}, booktitle = {Proceedings of the 2012 {{Conference}} on {{Design}} and {{Architectures}} for {{Signal}} and {{Image Processing}}}, @@ -2789,6 +3256,15 @@ file = {/home/jaseg/Sync/Research/Zotero/2012_Kryjak et al_FPGA implementation of camera tamper detection in real-time.pdf} } +@article{kvk2019, + title = {Internet of Things Based Monitoring of Large Rotor Vibration with a Microelectromechanical Systems Accelerometer}, + author = {Koene, Ivar and Viitala, Raine and Kuosmanen, Petri}, + date = {2019}, + journaltitle = {IEEE access : practical innovations, open solutions}, + shortjournal = {IEEE Access}, + doi = {10.1109/ACCESS.2019.2927793} +} + @article{kwekChipbasedQuantumKey2021, title = {Chip-Based Quantum Key Distribution}, author = {Kwek, Leong-Chuan and Cao, Lin and Luo, Wei and Wang, Yunxiang and Sun, Shihai and Wang, Xiangbin and Liu, Ai Qun}, @@ -2909,6 +3385,15 @@ file = {/home/jaseg/Zotero/storage/SPNJ8KBL/Launchbury et al. - 2014 - Application-Scale Secure Multiparty Computation.pdf} } +@inproceedings{ledger2019, + title = {Everybody Be Cool, This Is a Robbery!}, + booktitle = {Symposium Sur La Sécurité Des Technologies de l'information et Des Communications 2019}, + author = {Bédrune, Jean-Baptiste and Campana, Gabriel}, + date = {2019}, + url = {https://www.sstic.org/media/SSTIC2019/SSTIC-actes/hsm/SSTIC2019-Article-hsm-campana_bedrune_neNSDyL.pdf}, + urldate = {2021-09-24} +} + @inproceedings{lee16psresolutionRandomEquivalent2003, title = {A 16ps-Resolution {{Random Equivalent Sampling}} Circuit for {{TDR}} Utilizing a {{Vernier}} Time Delay Generation}, booktitle = {2003 {{IEEE Nuclear Science Symposium}}. {{Conference Record}} ({{IEEE Cat}}. {{No}}.{{03CH37515}})}, @@ -3265,6 +3750,24 @@ file = {/home/jaseg/Sync/Research/Zotero/Maier et al_2019_Contribution to the System Design of Contactless Energy Transfer Systems.pdf;/home/jaseg/Zotero/storage/Q4MPPLFH/8440726.html} } +@article{makarFormateAssayBody1975, + title = {Formate Assay in Body Fluids: Application in Methanol Poisoning}, + shorttitle = {Formate Assay in Body Fluids}, + author = {Makar, A. B. and McMartin, K. E. and Palese, M. and Tephly, T. R.}, + date = {1975-06}, + journaltitle = {Biochemical Medicine}, + shortjournal = {Biochem Med}, + volume = {13}, + number = {2}, + eprint = {1}, + eprinttype = {pmid}, + pages = {117--126}, + issn = {0006-2944}, + doi = {10.1016/0006-2944(75)90147-7}, + langid = {english}, + keywords = {Aldehyde Oxidoreductases,Animals,Body Fluids,Carbon Dioxide,Formates,Haplorhini,Humans,Hydrogen-Ion Concentration,Kinetics,Methanol,Methods,Pseudomonas} +} + @online{MakeYourElectronics, title = {Make {{Your Electronics Tamper-Evident}}}, url = {https://www.anarsec.guide/posts/tamper/}, @@ -3448,6 +3951,26 @@ file = {/home/jaseg/Zotero/storage/TMI3LX3I/Melara et al. - CONIKS Bringing Key Transparency to End Users.pdf} } +@online{mennChineseGovernmentHackers2024, + title = {Chinese Government Hackers Penetrate {{U}}.{{S}}. Internet Providers to Spy}, + author = {Menn, Joseph}, + date = {2024-08-27}, + url = {https://www.washingtonpost.com/technology/2024/08/27/chinese-government-hackers-penetrate-us-internet-providers-spy/}, + urldate = {2025-05-15}, + abstract = {Beijing’s hacking effort has “dramatically stepped up from where it used to be,” says former top U.S cybersecurity official.}, + langid = {american}, + organization = {The Washington Post}, + file = {/home/jaseg/Zotero/storage/4FLHNCC6/chinese-government-hackers-penetrate-us-internet-providers-spy.html} +} + +@www{mgchemicals2017, + title = {{{MG}} Chemicals Specialty Adhesives Catalog}, + author = {{MG Chemicals}}, + date = {2019}, + url = {https://www.mgchemicals.com/downloads/catalogs/Specialty%20Adhesives%20Catalogue%20Web.pdf}, + urldate = {2021-07-08} +} + @video{mikeselectricstuffNeopostPostalFranking2023, entrysubtype = {video}, title = {Neopost {{Postal Franking Machines}}}, @@ -3500,6 +4023,14 @@ file = {/home/jaseg/Zotero/storage/AM4Q8Y76/Mohan et al. - 1999 - Simple accurate expressions for planar spiral indu.pdf} } +@online{molexMolexSilverFlexible, + title = {Molex {{Silver Flexible Circuit Solutions}}}, + author = {{Molex}}, + url = {https://my.avnet.com/wcm/connect/d5fa4b27-de81-4aac-9bcb-cff3844b9eb3/Silver-Flexible-Circuit-Solutions-Brochure-EN-Brochure.pdf?MOD=AJPERES&CVID=oMyo8ki}, + urldate = {2025-05-07}, + file = {/home/jaseg/Zotero/storage/SY87W3RX/Silver-Flexible-Circuit-Solutions-Brochure-EN-Brochure.pdf} +} + @inproceedings{monfaredLeakyOhmSecretBits2023, title = {{{LeakyOhm}}: {{Secret Bits Extraction}} Using {{Impedance Analysis}}}, shorttitle = {{{LeakyOhm}}}, @@ -3872,6 +4403,14 @@ langid = {english} } +@www{newman2020, + title = {Apple's {{T2}} Security Chip Has an Unfixable Flaw}, + author = {Newman, Lily Hay}, + date = {2020-10-06}, + publisher = {Wired Magazine}, + url = {https://www.wired.com/story/apple-t2-chip-unfixable-flaw-jailbreak-mac/} +} + @article{nguyenReviewComparisonSolid2020, title = {A {{Review}} and {{Comparison}} of {{Solid}}, {{Multi-Strands}} and {{Litz Style PCB Winding}}}, author = {Nguyen, Minh Huy and Fortin Blanchette, Handy}, @@ -4047,6 +4586,25 @@ annotation = {2015 re-declassified version contains more material} } +@article{obermaier2018, + title = {The Past, Present, and Future of Physical Security Enclosures: {{From}} Battery-Backed Monitoring to {{PUF-based}} Inherent Security and Beyond}, + author = {Obermaier, Johannes and Immler, Vincent}, + date = {2018}, + journaltitle = {Journal of Hardware and Systems Security}, + volume = {2}, + pages = {289--296}, + issn = {2509-3428}, + doi = {10.1007/s41635-018-0045-2} +} + +@www{obermaier2019, + title = {Physical Unclonable Functions: {{The}} Future Technology for Physical Security Enclosures?}, + author = {Obermaier, Johannes}, + date = {2019-08-24}, + publisher = {Chaos Computer Club e.V.}, + doi = {10.5446/43265} +} + @article{obermaierBreakingRestoringEmbedded, title = {Breaking and {{Restoring Embedded System Security}} - {{From Practical Attacks}} to {{Novel PUF-Based Physical Security Enclosures}}}, author = {Obermaier, Johannes}, @@ -4094,6 +4652,18 @@ isbn = {978-1-5386-9210-3} } +@inproceedings{ongaro2019, + title = {In Search of an Understandable Consensus Algorithm}, + booktitle = {2014 {{USENIX}} Annual Technical Conference ({{USENIX ATC}} 14)}, + author = {Ongaro, Diego and Ousterhout, John}, + date = {2014-06}, + pages = {305--319}, + publisher = {USENIX Association}, + location = {Philadelphia, PA}, + url = {https://www.usenix.org/conference/atc14/technical-sessions/presentation/ongaro}, + isbn = {978-1-931971-10-2} +} + @article{orlandiOptimizationShieldedPCB2011, title = {Optimization of {{Shielded PCB Air-Core Toroids}} for {{High-Efficiency DC}}–{{DC Converters}}}, author = {Orlandi, Stefano and Allongue, Bruno Andre and Blanchot, Georges and Buso, Simone and Faccio, Federico and Fuentes, Cristian Alejandro and Kayal, Maher and Michelis, Stefano and Spiazzi, Giorgio}, @@ -4200,6 +4770,12 @@ file = {/home/jaseg/Zotero/storage/RLBAU32H/Patra et al. - ABY2.0 Improved Mixed-Protocol Secure Two-Party C.pdf} } +@article{PavingWayFull, + title = {Paving the {{Way}} to {{Full Security}} in {{eHealth}} – {{Ensuring}} Complete Security for Digital Data, Connected Environments and Devices in {{eHealth}}}, + langid = {english}, + file = {/home/jaseg/Zotero/storage/CCJFZZ34/Paving the Way to Full Security in eHealth – Ensur.pdf} +} + @standard{pcisecuritystandardscouncilPaymentCardIndustry2021, title = {Payment {{Card Industry PIN Transaction Security Hardware Security Module Modular Security Requirements}}}, author = {{PCI Security Standards Council}}, @@ -4227,6 +4803,15 @@ file = {/home/jaseg/Zotero/storage/QDJV4ERT/Perrig et al. - The TESLA Broadcast Authentication Protocol.pdf} } +@www{perrin2018, + title = {The Noise Protocol Framework}, + author = {Perrin, Trevor}, + date = {2018-07-11}, + url = {http://noiseprotocol.org/noise.html}, + urldate = {2021-07-13}, + version = {Revision 34} +} + @incollection{pinkasPSIPaXoSFast2020, title = {{{PSI}} from {{PaXoS}}: {{Fast}}, {{Malicious Private Set Intersection}}}, shorttitle = {{{PSI}} from {{PaXoS}}}, @@ -4310,6 +4895,17 @@ file = {/home/jaseg/Sync/Research/Zotero/Pirnay et al_2022_Learning classical readout quantum PUFs based on single-qubit gates.pdf} } +@inproceedings{plummerHistoryNuclearWeapon1998, + title = {The {{History}} of {{Nuclear Weapon Safety Devices}}}, + author = {Plummer, David W. and Greenwood, William H.}, + date = {1998}, + publisher = {Sandia National Laboratories}, + url = {https://www.osti.gov/servlets/purl/671923}, + urldate = {2025-04-16}, + eventtitle = {34th {{AIAA}}/{{ASME}}/{{SAE}}/{{ASEE Joint Propulsion Conference}}}, + file = {/home/jaseg/Zotero/storage/T6MZFXIB/671923.pdf} +} + @thesis{polasekReflektometrCasoveOblasti2020, type = {mathesis}, title = {Reflektometr v Časové Oblasti}, @@ -4353,6 +4949,24 @@ file = {/home/jaseg/Zotero/storage/WTJ3HBFT/o0485.html} } +@inproceedings{putzAcousticIntegrityCodes2020, + title = {Acoustic Integrity Codes: Secure Device Pairing Using Short-Range Acoustic Communication}, + shorttitle = {Acoustic Integrity Codes}, + booktitle = {Proceedings of the 13th {{ACM Conference}} on {{Security}} and {{Privacy}} in {{Wireless}} and {{Mobile Networks}}}, + author = {Putz, Florentin and Álvarez, Flor and Classen, Jiska}, + date = {2020-07-21}, + series = {{{WiSec}} '20}, + pages = {31--41}, + publisher = {Association for Computing Machinery}, + location = {New York, NY, USA}, + doi = {10.1145/3395351.3399420}, + url = {https://dl.acm.org/doi/10.1145/3395351.3399420}, + urldate = {2025-05-28}, + abstract = {Secure Device Pairing (SDP) relies on an out-of-band channel to authenticate devices. This requires a common hardware interface, which limits the use of existing SDP systems. We propose to use short-range acoustic communication for the initial pairing. Audio hardware is commonly available on existing off-the-shelf devices and can be accessed from user space without requiring firmware or hardware modifications.We improve upon previous approaches by designing Acoustic Integrity Codes (AICs): a modulation scheme that provides message authentication on the acoustic physical layer. We analyze their security and demonstrate that we can defend against signal cancellation attacks by designing signals with low autocorrelation. Our system can detect overshadowing attacks using a ternary decision function with a threshold. In our evaluation of this SDP scheme's security and robustness, we achieve a bit error ratio below 0.1\% for a net bit rate of 100 bps with a signal-to-noise ratio (SNR) of 14 dB. Using our open-source proof-of-concept implementation on Android smartphones, we demonstrate pairing between different smartphone models.}, + isbn = {978-1-4503-8006-5}, + file = {/home/jaseg/Sync/Research/Zotero/Putz et al_2020_Acoustic integrity codes.pdf} +} + @book{querfurthCoilWindingDescription1954, title = {Coil {{Winding}}: {{A Description}} of {{Coil Winding Procedures}}, {{Winding Machines}} and {{Associated Equipment}}}, author = {Querfurth, William}, @@ -4381,6 +4995,14 @@ file = {/home/jaseg/Sync/Research/Zotero/Quisquater_Samyde_2001_ElectroMagnetic Analysis (EMA).pdf} } +@patent{rahman1988, + type = {patentus}, + title = {Optical Fiber Cable with Tampering Detecting Means}, + author = {Rahman, Mujib}, + date = {1988-03-10}, + number = {Patent US4859024A} +} + @article{rahmanComprehensiveSurveyHardwareSoftware, title = {A {{Comprehensive Survey}} on {{Hardware-Software}} Co-{{Protection}} against {{Invasive}}, {{Non-Invasive}} and {{Interactive Security Threats}}}, author = {Rahman, Habibur}, @@ -4403,6 +5025,16 @@ file = {/home/jaseg/Sync/Research/Zotero/2020_Razaghi_Hill_Tamper detection system.pdf} } +@online{RefusingTechFascism, + title = {Refusing {{Tech Fascism}} — {{Error}} 406 {{Tech Fascism Not Acceptable}}}, + url = {https://error417.expectation.fail/406/tech-fascism-not-acceptable/essay-refusing-tech-fascism-by-tante}, + urldate = {2025-05-16}, + abstract = {An essay on Refusing Tech Fascism by Jürgen Geuter aka @tante}, + langid = {english}, + organization = {Error 417 Expectation Failed}, + file = {/home/jaseg/Zotero/storage/I6AG4WCP/essay-refusing-tech-fascism-by-tante.html} +} + @misc{renesaselectronicscorporationApplicationNoteAN2242019, title = {Application {{Note AN-224}}: {{ALVC}}/{{LVC Logic Characteristics}} and {{Applications}}}, author = {{Renesas Electronics Corporation}}, @@ -4612,6 +5244,22 @@ file = {/home/jaseg/Zotero/storage/LYZND7TS/Saeif et al. - 2023 - The Day-After-Tomorrow On the Performance of Radi.pdf} } +@article{sagarStudiesTemperatureDependent2024, + title = {Studies on Temperature Dependent Dielectric Properties of Some Insulators down to Liquid Helium Temperatures}, + author = {Sagar, Pankaj and Akber, Kashif}, + date = {2024-07-01}, + journaltitle = {Cryogenics}, + shortjournal = {Cryogenics}, + volume = {141}, + pages = {103865}, + issn = {0011-2275}, + doi = {10.1016/j.cryogenics.2024.103865}, + url = {https://www.sciencedirect.com/science/article/pii/S0011227524000857}, + urldate = {2025-07-14}, + abstract = {The work discusses on the behavior of dielectric properties of various commercially available insulators with respect to temperature (4.2 K to 300 K) and operating frequency range of 2.52 KHz to 500 KHz. A conventional parallel plate-based capacitor setup was designed and developed considering various conditions. The dielectric constant was found to be very dependent on the pre-breakdown partial discharges at low temperatures. At 4.2 K the discharges move far away from the electrodes and exert high electric stress on the sample under test, which results in the breakdown or the decrease in the dielectric strength. The relative permittivity (ϵr) also decreased rapidly with the increase in frequency in most of the samples, this decrease is due to the reduction of space charge polarization effect. The correlation between the dielectric properties, operating frequencies and temperature have been studied in detailed.}, + keywords = {Capacitor,Cold electronics,Insulators,Relative permittivity} +} + @article{samiAdvancingTrustworthinessSysteminPackage2024, title = {Advancing {{Trustworthiness}} in {{System-in-Package}}: {{A Novel Root-of-Trust Hardware Security Module}} for {{Heterogeneous Integration}}}, shorttitle = {Advancing {{Trustworthiness}} in {{System-in-Package}}}, @@ -4794,6 +5442,26 @@ file = {/home/jaseg/Sync/Research/Zotero/Seol et al_2016_A Trusted IaaS Environment with Hardware Security Module.pdf;/home/jaseg/Zotero/storage/ZFNE2NAZ/7010017.html} } +@article{sh2016, + title = {Application of {{MEMS}} Accelerometer for Detection and Diagnosis of Multiple Faults in the Roller Element Bearings of Three Phase Induction Motor}, + author = {S., Maruthi G. and Hegde, Vishwanath}, + date = {2016}, + journaltitle = {IEEE Sensors Journal}, + volume = {16}, + number = {1}, + issn = {1558-1748}, + doi = {10.1109/JSEN.2015.2476561}, + url = {https://www.researchgate.net/profile/Vishwanath-Hegde-2/publication/282389149_Application_of_MEMS_Accelerometer_for_Detection_and_Diagnosis_of_Multiple_Faults_in_the_Roller_Element_Bearings_of_Three_Phase_Induction_Motor/links/568bace808aebccc4e1c01fa/Application-of-MEMS-Accelerometer-for-Detection-and-Diagnosis-of-Multiple-Faults-in-the-Roller-Element-Bearings-of-Three-Phase-Induction-Motor.pdf} +} + +@book{shabany2009, + title = {Heat Transfer: {{Thermal}} Management of Electronics}, + author = {Shabany, Younes}, + date = {2009}, + publisher = {CRC Press}, + isbn = {978-1-4398-1468-0} +} + @article{shenDAENetMakingStrong2022, title = {{{DAENet}}: {{Making Strong Anonymity Scale}} in a {{Fully Decentralized Network}}}, shorttitle = {{{DAENet}}}, @@ -4881,6 +5549,32 @@ file = {/home/jaseg/Zotero/storage/S2TLFNT7/Sifferman et al. - 2023 - Unlocking the Performance of Proximity Sensors by .pdf} } +@www{signal2019, + title = {Technology {{Preview}} for Secure Value Recovery}, + author = {Lund, Joshua}, + date = {2019-12-19}, + publisher = {signal.org / signal.org}, + url = {https://signal.org/blog/secure-value-recovery/}, + urldate = {2021-07-12} +} + +@article{simmonsHowInsureThat1988, + title = {How to Insure That Data Acquired to Verify Treaty Compliance Are Trustworthy}, + author = {Simmons, G.J.}, + date = {1988-05}, + journaltitle = {Proceedings of the IEEE}, + volume = {76}, + number = {5}, + pages = {621--627}, + issn = {1558-2256}, + doi = {10.1109/5.4446}, + url = {https://ieeexplore.ieee.org/document/4446/}, + urldate = {2025-06-26}, + abstract = {The author presents a solution to the problem of how to make it possible for two mutually distrusting (and presumed deceitful) parties, the host and the monitor, to both trust a data acquisition system that informs the monitor and perhaps third parties, whether the host has or has not violated the terms of a treaty. He starts by assuming that such a data acquisition system exists, and that the opportunities for deception lie only in the manipulation, i.e. forgery, modification, retransmission, etc. The author shows that it is possible to satisfy simultaneously the interests of all parties. The technical device on which this resolution depends is the concatenation of two or more private authentication channels to create a system in which each participant need only trust that part of the whole that he or she contributed. In the resulting scheme, no part of the data need to be kept secret from any participant at any time; no party nor collusion of fewer than all of the parties can utter an undetectable forgery; no unilateral action on the part of any party can lessen the confidence of others as to the authenticity of the data, and third parties, i.e. arbiters, can be logically persuaded of the authenticity of data.{$<>$}}, + keywords = {Arm,Computer security,Computer Society,Control systems,Data acquisition,Forgery,Laboratories,Monitoring,Nuclear weapons,System testing}, + file = {/home/jaseg/Sync/Research/Zotero/Simmons_1988_How to insure that data acquired to verify treaty compliance are trustworthy.pdf} +} + @article{skorobogatovHardwareSecurityImplications2018, title = {Hardware {{Security Implications}} of {{Reliability}}, {{Remanence}}, and {{Recovery}} in {{Embedded Memory}}}, author = {Skorobogatov, Sergei}, @@ -4900,6 +5594,40 @@ file = {/home/jaseg/Sync/Research/Zotero/2018_Skorobogatov_Hardware Security Implications of Reliability, Remanence, and Recovery in.pdf} } +@online{slanySicherheitsanalyseZurSicherheit2020, + title = {Sicherheitsanalyse zur Sicherheit der kritischen Komponenten der elektronischen Patientenakte nach §291a SGB V}, + author = {Slany, Wolfgang}, + date = {2020-03}, + url = {https://www.gematik.de/media/gematik/Medien/Newsroom/Presse/Dokumente/Sicherheitsanalyse_TU_Graz_zur_ePA_mit_Vorwort_der_gematik.pdf}, + urldate = {2025-05-15}, + langid = {german}, + file = {/home/jaseg/Zotero/storage/SVMJG2SZ/Sicherheitsanalyse_TU_Graz_zur_ePA_mit_Vorwort_der_gematik.pdf} +} + +@online{SmaugDracheUnd, + title = {Smaug, der Drache, und die ePA: Ein zentraler Schlüsselgenerierungsdienst, ein zentrales Risiko}, + shorttitle = {Smaug, der Drache, und die ePA}, + url = {https://de.linkedin.com/pulse/smaug-der-drache-und-die-epa-ein-zentraler-zentrales-risiko-block-vh3ue}, + urldate = {2025-05-10}, + abstract = {Stell Dir vor, wir befinden uns in Tolkiens Welt von Der Hobbit: Smaug, der mächtige Drache, liegt auf einem Berg aus Gold, überzeugt davon, dass er unbesiegbar ist. Doch in seiner scheinbar uneinnehmbaren Festung gibt es eine winzige Schwachstelle – eine kleine Stelle in seinem Panzer.}, + langid = {ngerman}, + annotation = {Archive 1: https://archive.is/PVJO8\\ +Archive 2: https://web.archive.org/web/20250510104017/https://de.linkedin.com/pulse/smaug-der-drache-und-die-epa-ein-zentraler-zentrales-risiko-block-vh3ue}, + file = {/home/jaseg/Zotero/storage/FIPZSEGC/smaug-der-drache-und-die-epa-ein-zentraler-zentrales-risiko-block-vh3ue.html} +} + +@article{smith1998, + title = {Building a High-Performance, Programmable Secure Coprocessor}, + author = {Smith, Sean and Weingart, Steve}, + date = {1999}, + journaltitle = {Computer Networks}, + volume = {31}, + number = {8}, + publisher = {IBM T.J. Watson Research Center}, + url = {ftp://www6.software.ibm.com/software/cryptocards/rc21102.pdf}, + urldate = {2020-09-16} +} + @article{smithDesignOptimizationVoice2015, title = {Design and {{Optimization}} of a {{Voice Coil Motor With}} a {{Rotary Actuator}} for an {{Ultrasound Scanner}}}, author = {Smith, Kristopher J. and Graham, David J. and Neasham, Jeffrey A.}, @@ -4994,6 +5722,14 @@ file = {/home/jaseg/Sync/Research/Zotero/2021_Sozio et al_Patchable Hardware Security Module (PHaSM) for Extending FPGA Root-of-Trust.pdf;/home/jaseg/Zotero/storage/D5BLNRV7/9707698.html} } +@standard{SpezifikationFachmodulEPA2023, + title = {Spezifikation Fachmodul ePA}, + date = {2023-04-03}, + langid = {ngerman}, + version = {1.53.0}, + file = {/home/jaseg/Zotero/storage/J79W78KS/Spezifikation Fachmodul ePA.pdf} +} + @article{sproHighVoltageInsulationDesign2021, title = {High-{{Voltage Insulation Design}} of {{Coreless}}, {{Planar PCB Transformers}} for {{Multi-MHz Power Supplies}}}, author = {Spro, Ole Christian and Mauseth, Frank and Peftitsis, Dimosthenis}, @@ -5200,7 +5936,7 @@ doi = {10.1007/978-3-031-19185-5}, url = {https://link.springer.com/10.1007/978-3-031-19185-5}, urldate = {2025-04-04}, - isbn = {978-3-031-19184-8 978-3-031-19185-5}, + isbn = {978-3-031-19184-8}, langid = {english} } @@ -5213,7 +5949,7 @@ doi = {10.1007/978-1-4419-8080-9}, url = {https://link.springer.com/10.1007/978-1-4419-8080-9}, urldate = {2024-12-13}, - isbn = {978-1-4419-8079-3 978-1-4419-8080-9}, + isbn = {978-1-4419-8079-3}, langid = {english}, file = {/home/jaseg/Zotero/storage/QX3DYZC3/Tehranipoor and Wang - 2012 - Introduction to Hardware Security and Trust.pdf} } @@ -5227,6 +5963,41 @@ file = {/home/jaseg/Zotero/storage/SXP7TBFQ/070-1128-01_1987.pdf} } +@www{terdiman2013, + title = {Aboard {{America}}'s {{Doomsday}} Command and Control Plane}, + author = {Terdiman, Daniel}, + year = {2013-07-23, 2013-07}, + publisher = {CNET / cnet.com}, + url = {https://www.cnet.com/news/aboard-americas-doomsday-command-and-control-plane} +} + +@www{thales2015hsmha, + title = {{{SafeNet PCI-e HSM}} 6.2 Product Documentation: {{High}} Availability ({{HA}}) Overview}, + author = {NV, Gemalto}, + date = {2015-12-18}, + publisher = {Gemalto NV}, + url = {https://thalesdocs.com/gphsm/luna/6.2/docs/pci/Content/administration/ha/ha_overview.htm}, + urldate = {2021-07-12} +} + +@www{thales2021, + title = {Thales Luna {{HSM}} Product Family Overview Page}, + author = {Group, Thales}, + date = {2021}, + publisher = {Thales Group}, + url = {https://cpl.thalesgroup.com/encryption/hardware-security-modules/network-hsms}, + urldate = {2021-07-08} +} + +@article{tobisch2020, + title = {Electromagnetic Enclosure {{PUF}} for Tamper Proofing Commodity Hardware and Other Applications}, + author = {Tobisch, Johannes and Zenger, Christian and Paar, Christof}, + date = {2020-03-13}, + journaltitle = {TRUDEVICE 2020: 9th Workshop on Trustworthy Manufacturing and Utilization of Secure Devices}, + url = {https://www.emsec.ruhr-uni-bochum.de/media/crypto/veroeffentlichungen/2020/05/13/trudevice_submission_enclosure_puf.pdf}, + urldate = {2020-09-17} +} + @article{tobischPhysicalSystemsIntegrity, title = {Physical Systems for Integrity Protection and Authentication}, author = {Tobisch, Johannes}, @@ -5240,6 +6011,20 @@ file = {/home/jaseg/Zotero/storage/TLI54XGI/Tobisch - Physical systems for integrity protection and auth.pdf} } +@article{tolkSafeguardsSensorsSystems2007, + title = {Safeguards {{Sensors}} and {{Systems}}: {{Past}}, {{Present}}, and {{Future}}}, + shorttitle = {Safeguards {{Sensors}} and {{Systems}}}, + author = {Tolk, Keith and Mangan, Dennis and Glidewell, Don and Matter, John and Whichello, Julian}, + date = {2007-07-01}, + journaltitle = {Journal of Nuclear Materials Management}, + shortjournal = {Journal of Nuclear Materials Management}, + volume = {35}, + number = {4}, + pages = {101--110}, + issn = {0893-6188}, + abstract = {Sensors are a vital and critical element in measuring and monitoring systems for technical safeguards approaches. Safeguards sensors have evolved from standalone analog devices to integrated digital systems. Safeguards sensor technologies are a niche market that has been driven by other commercial and military demands and applications. Developers and manufacturers have successfully adapted technologies of the day to be effective products for safeguards applications. In this paper commemorating the first fifty years of the International Atomic Energy Agency and its role in the peaceful uses of atomic energy and international safeguards, we highlight the evolution of sensor technologies applied to international safeguards. This history began with the use of cameras and seals for containment and surveillance to maintain continuity of knowledge on safeguarded materials and activities. The current international safeguards norm is based on a combination of onsite verification measures and unattended and remote measurement and monitoring systems. The near-term need for detection of undeclared nuclear materials, facilities, and activities will likely be addressed by the engineering development of several novel technologies. The long-range development of safeguards sensor systems will be shaped by research in materials, computing, and communication technologies.} +} + @article{trebbelsMiniaturizedFPGABasedHighResolution2013, title = {Miniaturized {{FPGA-Based High-Resolution Time-Domain Reflectometer}}}, author = {Trebbels, Dennis and Kern, Alois and Fellhauer, Felix and Huebner, Christof and Zengerle, Roland}, @@ -5257,6 +6042,49 @@ file = {/home/jaseg/Zotero/storage/ZCJLJ7JB/6484979.html} } +@inproceedings{trippel2017, + title = {{{WALNUT}}: {{Waging}} Doubt on the Integrity of {{MEMS}} Accelerometers with Acoustic Injection Attacks}, + booktitle = {2017 {{IEEE European}} Symposium on Security and Privacy}, + author = {Trippel, Timothy and Weisse, Ofir and Xu, Wenyuan and Honeyman, Peter and Fu, Kevin}, + date = {2017}, + pages = {3--18}, + publisher = {IEEE}, + x-fetchedfrom = {Google Scholar} +} + +@online{tschirsichHackerHinOder0100, + title = {"{{Hacker}} Hin Oder Her": {{Die}} Elektronische {{Patientenakte}} Kommt!}, + shorttitle = {"{{Hacker}} Hin Oder Her"}, + author = {Tschirsich, Martin and Brodowski, cbro-Dr med Christian and Zilch, Dr André}, + year = {01:00:00 +0100}, + url = {https://media.ccc.de/v/36c3-10595-hacker_hin_oder_her_die_elektronische_patientenakte_kommt}, + urldate = {2025-05-15}, + abstract = {Herzstück der digitalen Gesundheitsversorgung für 73 Millionen Versicherte ist die hochsichere, kritische Telematik-Infrastruktur mit ber...}, + langid = {english}, + file = {/home/jaseg/Zotero/storage/XVJB3U43/36c3-10595-hacker_hin_oder_her_die_elektronische_patientenakte_kommt.html} +} + +@online{tschirsichKonnteBisherNoch0100, + title = {„{{Konnte}} Bisher Noch Nie Gehackt Werden“: {{Die}} Elektronische {{Patientenakte}} Kommt - Jetzt Für Alle!}, + shorttitle = {„{{Konnte}} Bisher Noch Nie Gehackt Werden“}, + author = {Tschirsich, Martin and Kastl, Bianca}, + year = {00:00:00 +0100}, + url = {https://media.ccc.de/v/38c3-konnte-bisher-noch-nie-gehackt-werden-die-elektronische-patientenakte-kommt-jetzt-fr-alle}, + urldate = {2025-05-15}, + abstract = {In wenigen Wochen werden die Gesundheitsdaten von rund 73 Millionen in Deutschland Krankenversicherten ohne deren Zutun über Praxis- und ...}, + langid = {english}, + file = {/home/jaseg/Zotero/storage/FYNQN7QX/38c3-konnte-bisher-noch-nie-gehackt-werden-die-elektronische-patientenakte-kommt-jetzt-fr-alle.html} +} + +@inproceedings{tschofenig2015, + title = {Performance of State-of-the-Art Cryptography on {{ARM-based}} Microprocessors}, + booktitle = {{{NIST}} Lightweight Cryptography Workshop 2015}, + author = {Tschofenig, Hannes and Pegourie-Gonnard, Manuel and Vincent, Hugo}, + date = {2015-07-21}, + url = {https://csrc.nist.gov/csrc/media/events/lightweight-cryptography-workshop-2015/documents/presentations/session7-vincent.pdf}, + urldate = {2021-07-13} +} + @article{tyagiOrcaBlocklistingSenderAnonymous, title = {Orca: {{Blocklisting}} in {{Sender-Anonymous Messaging}}}, author = {Tyagi, Nirvan and Len, Julia and Miers, Ian and Ristenpart, Thomas}, @@ -5279,6 +6107,20 @@ file = {/home/jaseg/Sync/Research/Zotero/2002_Technology_Security Requirements for Cryptographic Modules.pdf} } +@report{usnationalinstituteofstandardsandtechnologySecurityRequirementsCryptographic2019, + title = {Security {{Requirements}} for {{Cryptographic Modules}}}, + author = {{(US) National Institute of Standards and Technology}}, + date = {2019-03-22}, + number = {Federal Information Processing Standard (FIPS) 140-3}, + institution = {U.S. Department of Commerce}, + doi = {10.6028/NIST.FIPS.140-3}, + url = {https://csrc.nist.gov/pubs/fips/140-3/final}, + urldate = {2025-05-15}, + abstract = {The selective application of technological and related procedural safeguards is an important responsibility of every federal organization in providing adequate security in its computer and telecommunication systems.~ ~This standard is applicable to all federal agencies that use cryptographic-based security systems to protect sensitive information in computer and telecommunication systems (including voice systems) as defined in Section 5131 of the Information Technology Management Reform Act of 1996, Public Law 104-106 and the Federal Information Security Management Act of 2002, Public Law 107-347.~ This standard shall be used in designing and implementing cryptographic modules that federal departments and agencies operate or are operated for them under contract.~ The standard provides four increasing, qualitative levels of security intended to cover a wide range of potential applications and environments.~ The security requirements cover areas related to the secure design,...}, + langid = {english}, + file = {/home/jaseg/Sync/Research/Zotero/2019_Technology_Security Requirements for Cryptographic Modules.pdf} +} + @inproceedings{uzunCryptographicKeyDerivation2021, title = {Cryptographic {{Key Derivation}} from {{Biometric Inferences}} for {{Remote Authentication}}}, booktitle = {Proceedings of the 2021 {{ACM Asia Conference}} on {{Computer}} and {{Communications Security}}}, @@ -5421,6 +6263,15 @@ file = {/home/jaseg/Zotero/storage/EGDJZN37/Voloshynovskiy et al. - 2006 - Information-theoretic analysis of electronic and p.pdf} } +@thesis{vrijaldenhoven2004, + type = {mathesis}, + title = {Acoustical Physical Uncloneable Functions}, + author = {Vrijaldenhoven, Serge}, + date = {2004-10-01}, + institution = {Technische Universiteit Eindhoven}, + url = {https://pure.tue.nl/ws/files/46971492/600055-1.pdf} +} + @article{vuDesignPerformanceRelayAssisted2020, title = {Design and {{Performance}} of {{Relay-Assisted Satellite Free-Space Optical Quantum Key Distribution Systems}}}, author = {Vu, Minh Quang and Pham, Thanh V. and Dang, Ngoc T. and Pham, Anh T.}, @@ -5641,6 +6492,26 @@ file = {/home/jaseg/Zotero/storage/S93U8AF3/Wang et al. - 2020 - Topological optimization of hybrid quantum key dis.pdf} } +@article{wangTwinfieldQuantumKey2022, + title = {Twin-Field Quantum Key Distribution over 830-Km Fibre}, + author = {Wang, Shuang and Yin, Zhen-Qiang and He, De-Yong and Chen, Wei and Wang, Rui-Qiang and Ye, Peng and Zhou, Yao and Fan-Yuan, Guan-Jie and Wang, Fang-Xiang and Chen, Wei and Zhu, Yong-Gang and Morozov, Pavel V. and Divochiy, Alexander V. and Zhou, Zheng and Guo, Guang-Can and Han, Zheng-Fu}, + date = {2022-02}, + journaltitle = {Nature Photonics}, + shortjournal = {Nat. Photon.}, + volume = {16}, + number = {2}, + pages = {154--161}, + publisher = {Nature Publishing Group}, + issn = {1749-4893}, + doi = {10.1038/s41566-021-00928-2}, + url = {https://www.nature.com/articles/s41566-021-00928-2}, + urldate = {2025-05-08}, + abstract = {Quantum key distribution (QKD) provides a promising solution for sharing information-theoretic secure keys between remote peers with physics-based protocols. According to the law of quantum physics, the photons carrying signals cannot be amplified or relayed via classical optical techniques to maintain quantum security. As a result, the transmission loss of the channel limits its achievable distance, and this has been a huge barrier towards building large-scale quantum-secure networks. Here we present an experimental QKD system that could tolerate a channel loss beyond 140\,dB and obtain a secure distance of 833.8\,km, setting a new record for fibre-based QKD. Furthermore, the optimized four-phase twin-field protocol and high-quality set-up make its secure key rate more than two orders of magnitude greater than previous records over similar distances. Our results mark a breakthrough towards building reliable and efficient terrestrial quantum-secure networks over a scale of 1,000\,km.}, + langid = {english}, + keywords = {Quantum information,Single photons and quantum effects}, + file = {/home/jaseg/Zotero/storage/FCHS9D49/Wang et al. - 2022 - Twin-field quantum key distribution over 830-km fi.pdf} +} + @article{wegmanNewHashFunctions1981, title = {New Hash Functions and Their Use in Authentication and Set Equality}, author = {Wegman, Mark N. and Carter, J.Lawrence}, @@ -5755,6 +6626,24 @@ file = {/home/jaseg/Sync/Research/Zotero/1965_Wheeler_Transmission-Line Properties of Parallel Strips Separated by a Dielectric Sheet.pdf;/home/jaseg/Zotero/storage/J6YQL49I/1125962.html} } +@article{wiesmannEffectChloroquineCultured1975, + title = {Effect of Chloroquine on Cultured Fibroblasts: Release of Lysosomal Hydrolases and Inhibition of Their Uptake}, + shorttitle = {Effect of Chloroquine on Cultured Fibroblasts}, + author = {Wiesmann, U. N. and DiDonato, S. and Herschkowitz, N. N.}, + date = {1975-10-27}, + journaltitle = {Biochemical and Biophysical Research Communications}, + shortjournal = {Biochem Biophys Res Commun}, + volume = {66}, + number = {4}, + eprint = {4}, + eprinttype = {pmid}, + pages = {1338--1343}, + issn = {1090-2104}, + doi = {10.1016/0006-291x(75)90506-9}, + langid = {english}, + keywords = {Biological Transport,Cells Cultured,Cerebroside-Sulfatase,Chloroquine,Dextrans,Fibroblasts,Glucuronidase,Humans,Leukodystrophy Metachromatic,Lysosomes,Pinocytosis,Skin,Sulfatases} +} + @book{wiggeRundfunktechnischesHandbuch1930, title = {Rundfunktechnisches {{Handbuch}}}, author = {Wigge, Heinrich}, @@ -5765,6 +6654,24 @@ keywords = {twisted-inductors} } +@article{worathumrongEffectOsalicylatePentose1975, + title = {The Effect of O-Salicylate upon Pentose Phosphate Pathway Activity in Normal and {{G6PD-deficient}} Red Cells}, + author = {Worathumrong, N. and Grimes, A. J.}, + date = {1975-06}, + journaltitle = {British Journal of Haematology}, + shortjournal = {Br J Haematol}, + volume = {30}, + number = {2}, + eprint = {35}, + eprinttype = {pmid}, + pages = {225--231}, + issn = {0007-1048}, + doi = {10.1111/j.1365-2141.1975.tb00536.x}, + abstract = {The effect of the major metabolite of aspirin, namely salicylic acid, upon the pentose phosphate pathway (PPP) of normal and G6PD-deficient red cells has been studied. Salicylic acid was shown to inhibit this pathway in proportion to the amount present. At any concentration of this substance there was greater inhibition of the PPP in G6PD-deficient than in normal red cells.}, + langid = {english}, + keywords = {Blood Glucose,Erythrocytes,Glucosephosphate Dehydrogenase Deficiency,Humans,Hydrogen-Ion Concentration,Methylene Blue,Pentosephosphates,Sodium Salicylate} +} + @article{wuGenericServeraidedSecure2022, title = {Generic Server-Aided Secure Multi-Party Computation in Cloud Computing}, author = {Wu, Yulin and Wang, Xuan and Susilo, Willy and Yang, Guomin and Jiang, Zoe L. and Yiu, Siu-Ming and Wang, Hao}, @@ -5954,6 +6861,19 @@ file = {/home/jaseg/Zotero/storage/CJXHEBEI/9390130.html} } +@article{yoshimitsu1990, + title = {A New Attenuation Relation for Peak Horizontal Acceleration of Strong Earthquake Ground Motion in {{Japan}}}, + author = {Fukushima, Yoshimitsu and Tanaka, Teiji}, + date = {1990}, + journaltitle = {Bulletin of the Seismological Society of America}, + volume = {80}, + number = {4}, + pages = {757--783}, + issn = {0037-1106}, + url = {https://pubs.geoscienceworld.org/ssa/bssa/article-abstract/80/4/757/102395/A-new-attenuation-relation-for-peak-horizontal}, + urldate = {2021-07-07} +} + @article{yuSecretKeyProvisioningCollaborative2022, title = {Secret-{{Key Provisioning With Collaborative Routing}} in {{Partially-Trusted-Relay-based Quantum-Key-Distribution-Secured Optical Networks}}}, author = {Yu, Xiaosong and Liu, Yuhang and Zou, Xingyu and Cao, Yuan and Zhao, Yongli and Nag, Avishek and Zhang, Jie}, @@ -6196,6 +7116,23 @@ file = {/home/jaseg/Zotero/storage/ZAQTS252/Zhao et al. - 2023 - Design and Optimization of Litz-Wire Planar Spiral.pdf} } +@article{zhouHiddenVoiceCommands2019, + title = {Hidden {{Voice Commands}}: {{Attacks}} and {{Defenses}} on the {{VCS}} of {{Autonomous Driving Cars}}}, + shorttitle = {Hidden {{Voice Commands}}}, + author = {Zhou, Man and Qin, Zhan and Lin, Xiu and Hu, Shengshan and Wang, Qian and Ren, Kui}, + date = {2019-10}, + journaltitle = {IEEE Wireless Communications}, + volume = {26}, + number = {5}, + pages = {128--133}, + issn = {1558-0687}, + doi = {10.1109/MWC.2019.1800477}, + url = {https://ieeexplore.ieee.org/abstract/document/8694199}, + urldate = {2025-05-28}, + abstract = {Autonomous driving is becoming one of the most popular applications of AI. Meanwhile, the advances in deep learning have promoted the rapid development of the voice controllable systems (VCSs), which have almost reached the maturity stage. Before autonomous driving cars reach the highest level of automation, intelligent voice interaction remains the primary approach for human-vehicle interaction. Recent works show that such intelligent systems are vulnerable to hidden voice commands that are unnoticed or unintelligible to humans. In particular, an adversary utilizing hidden voice commands is able to control autonomous driving cars. For example, malicious voice commands embedded into the sound of online shared videos can stealthily control the vehicle when people watch the videos in the car. In this article, we investigate the potential perniciousness of hidden voice commands on the VCS of autonomous driving cars, and then discuss feasible defense strategies. We finally propose a pop-noisebased general defense strategy that can resist various kinds of attacks.}, + keywords = {Automobiles,Autonomous vehicles,Machine learning,Microphones,Speech recognition,Ultrasonic imaging,Videos} +} + @inproceedings{zhouPPMLACHighPerformance2022, title = {{{PPMLAC}}: High Performance Chipset Architecture for Secure Multi-Party Computation}, shorttitle = {{{PPMLAC}}}, diff --git a/paper/paper.tex b/paper/paper.tex index fe64e5c..d523867 100644 --- a/paper/paper.tex +++ b/paper/paper.tex @@ -30,7 +30,7 @@ \usepackage{float} \definecolor{highlightred}{rgb}{0.6 0.1 0.1} -\definecolor{highlightgreen}{rgb}{0.12 0.5 0.07} +\definecolor{highlightgreen}{rgb}{0.12 0.4 0.07} \DeclareSIUnit{\baud}{Bd} \DeclareSIUnit{\year}{a} \DeclareSIUnit{\rpm}{rpm} @@ -593,35 +593,28 @@ oversampling. \section{Experimental Evaluation} -To validate our design, we performed a two-fold evaluation. First, we measured the performance of our sampling circuit -as a time-domain reflectometer. The most relevant figure to our mesh monitoring application is the pulse generators' -rise time, which determines the frontend's bandwidth and consequently the level of detail that we are able to extract -from a connected mesh during one scan. Since we aim at fingerprinting a connected mesh, not at performing absolute -measurements, we do not need to characterize or de-embed the transfer function of our TDR frontend. +We evaluated our design in two phases. In the first phase, we measured the electrical performance of our sampling +circuit. The key figure in our application is the pulse generators' rise time, which determines the level of detail that +we are able to extract. Since we aim at fingerprinting a connected mesh, not at performing absolute measurements, we do +not need to characterize or de-embed the transfer function of our TDR frontend. -Second, we characterized the end-to-end performance of our design on a mesh test specimen, and we evaluated its -performance on several realistic tamper attempts. As a baseline characterization, in Section\ \ref{sec_attack_short} we -will show measurements of both short and open mesh traces, allowing us to evaluate our designs' capacity to spatially -localize faults. Building upon this baseline, in Section\ \ref{sec_attack_probe} we will then demonstrate a probing -attack, in which we measured our design's response to a standard \qty{100}{\mega\hertz} bandwidth -$\qty{10}{\mega\ohm}||\qty{10}{\pico\farad}$ oscilloscope probe. Compared to the baseline open/short test, this provides -a greater challenge due to the probe's intentionally high impedance and minimal capacitive loading. Concluding our -attack tests, in Section\ \ref{sec_attack_bridge} we demonstrate a bridging attack that attempts to repair a break -created in the mesh through drilling. +In the second phase, we evaluated the actual performance of our design on a set of 500 mesh test specimens of different +layouts and structure sizes. We include detailed performance figures for a simple baseline classifier for attack +detection. +% FIXME more intro here \subsection{Rise Time Measurement} -We measured two figures of merit to characterize frontend speed. First, as shown in Section\ \ref{sec_spec_risetime} -below, we measured pulse rise time at the mesh interface to evaluate the raw rise time of our pulse generator. Second, -we used our circuit to perform a TDR measurement of a mesh test specimen and measured the rise time of the sampling -pulse as seen by the circuit itself. This figure indicates the actual measurement performance of our circuit. Both rise -times differ because of the non-linear characteristic of the sampling Schottky pairs. Depending on the IC, our pulse -generator produces output waveforms with \qtyrange{470}{3200}{\milli\volt} differential voltage swing. Since the -sampling diode pairs start to conduct at a combined forward voltage of approximately \qty{300}{\milli\volt}, they will -transition from high impedance to low impedance during a corresponding \qty{300}{\milli\volt} window at the middle of -the strobe pulse's edge. Thus, even if the strobe pulse shows a low-pass response with rounding at both ends, as long as -its slew rate $\frac{\mathrm{d}V}{\mathrm{d}t}$ during the zero crossing is fast enough, the pulse will still result in -a sharp turn-on knee of the sampling diodes. +The level of detail our frontend can extract from a mesh is limited by the rise time of the pulses it generates. We +characterized this rise time both externally, using a wideband spectrum analyzer (Section~\ref{sec_spec_risetime}), and +through self-characterization of the circuit (Section~\ref{sec_spec_risetime_selfchar}). Both measurements differ +because of the non-linear characteristic of the sampling Schottky pairs. Depending on the IC, our pulse generator +produces output waveforms with \qtyrange{470}{3200}{\milli\volt} differential voltage swing. Since the sampling diode +pairs start to conduct at a combined forward voltage of approximately \qty{300}{\milli\volt}, they will transition from +high impedance to low impedance during a corresponding \qty{300}{\milli\volt} window at the middle of the strobe pulse's +edge. Thus, even if the strobe pulse shows a low-pass response with rounding at both ends, as long as its slew rate +$\frac{\mathrm{d}V}{\mathrm{d}t}$ during the zero crossing is fast enough, the pulse will still result in a sharp +turn-on knee of the sampling diodes. \subsubsection{Stimulus Pulse Rise Time at the Mesh} \label{sec_spec_risetime} @@ -659,10 +652,10 @@ a sharp turn-on knee of the sampling diodes. \end{subfigure} \end{center} \vspace*{-5mm} - \caption{Spectrum measurements and re-constructed time domain pulse edge shape of the stimulus pulse measured at the - mesh interface for each of the four driver ICs. Amplitudes were normalized for rise time plots. The $\frac{1}{f}$ - curve in the spectrum plots shows the peak amplitude of the frequency components of an ideal infinite-bandwidth - square wave. The horizontal gray lines in the time domain plots show thresholds used for rise time calculation.} + \caption{Spectrum measurements and reconstructed time domain edge shape of the stimulus pulse measured at the + mesh interface for each of the four driver ICs. Vertical scale shows arbitrary units. Spectrum plots include a + $\frac{1}{f}$ curve indicating the frequency components of an ideal infinite-bandwidth square wave. Horizontal gray + lines in the time domain plots indicate thresholds used for rise time calculation.} \label{fig_spec_risetime} \end{figure} @@ -671,21 +664,19 @@ using a Keysight N9020A MXA \qty{26.5}{\giga\hertz} signal analyzer\footnote{The exceeded the capabilities of the fastest oscilloscopes we had access to, so it was the more appropriate choice of measurement instrument.}. All measurements were taken with the prototype's mesh interface connected to the spectrum analyzer through a bias tee configured for DC blocking followed by a \qty{20}{\deci\bel} attenuator for protection. -Since both stimulus and sampling pulses are generated using identical circuits, we can transfer those results to the -sampling pulse modulo amplifier output loading effects. -Figure\ \ref{fig_spec_risetime} and Table\ \ref{tab_edge_risetime} show the resulting measurements. For ease of -interpretation, we projected the measurements from the frequency domain (upper traces) back into the time domain (lower -traces), and extracted rise time measurements from those traces. Our measurements show that, as expected, the bare -\partno{74LVC}-series logic gate has the slowest rise time at approximately \qty{500}{\pico\second}. All three amplifier -variants we implemented showed significantly improved rise time, with the \partno{PI4HDX12211} achieving below -\qty{200}{\pico\second}, and the other two showing around \qty{120}{\pico\second}. A noteworthy detail is that -\partno{MAX3748} and \partno{TDP0604} only achieved a low output signal amplitude, which stems from a combination of -them having low output amplitude by design and of our circuit loading their outputs heavily. Since their amplitude is -only marginally within the knee region of the RF Schottky diodes used in the sampling bridges, in these variants, -the sampling gates end up slower than the raw pulse rise time value alone would suggest. +Figure\ \ref{fig_spec_risetime} and Table\ \ref{tab_edge_risetime} show the resulting measurements both in the frequency +domain (upper traces), and projected back into the time domain (lower traces) along with measured rise times. As +expected, the bare \partno{74LVC}-series logic gate has the slowest rise time at approximately \qty{500}{\pico\second}. +All three amplifier variants we implemented showed significantly improved rise time, with the \partno{PI4HDX12211} +achieving below \qty{200}{\pico\second}, and the other two showing around \qty{120}{\pico\second}. \partno{MAX3748} and +\partno{TDP0604} only achieved a low output signal amplitude, which stems from a combination of them having low output +amplitude by design and of our circuit loading their outputs heavily. Since their amplitude is only marginally within +the knee region of the RF Schottky diodes used in the sampling bridges, in these variants, the sampling gates end up +slower than the raw pulse rise time value alone would suggest. \subsubsection{Self-Characterization} +\label{sec_spec_risetime_selfchar} \begin{figure} \begin{center} @@ -737,43 +728,41 @@ the sampling gates end up slower than the raw pulse rise time value alone would \label{tab_edge_risetime} \end{table} -Figure\ \ref{fig_edge_risetime} shows the result of our self-characterization experiments, where we used the frontend to -measure its own pulse shape. These results correspond to the actual rise time we can expect in practical measurements. -In these experiments, we ran a measurement using $256\times$ oversampling at \qty{12}{b} ADC resolution. The plots show -voltage at the amplifier output voltage against time in \unit{\nano\second}. The absolute value of the amplifier output -voltage is not relevant here - only the rise time is. Since we use some of these amplifiers--particularly the redriver -ICs--well outside of their intended application, the actual voltage they develop across the nonlinear load that our -sampling gate's diode bridge presents depends on implementation details of the amplifier's CML output stage. To maximize -ADC resolution and minimize ringing, we tuned gain and bandwidth of each post-sampling amplifier for each IC. Ringing in -the amplifier output leads to jitter in the ADC's sampling period to directly feeding through to the ADC output value. -Since in \partno{STM32} MCUs, the ADC is clocked independently of the rest of the system, its sampling timing is poorly -controlled and this jitter causes a significant error unless the amplifier is well-compensated. The key figure for us is -how fast our sampling gate turns on, not how hard, so we can largely ignore the units on the graph's vertical scale. +While a fast edge is a necessary component for a fast sampling gate, the concrete speed of the sampling gate also +depends on other factors such as the pulse's amplitude. Figure\ \ref{fig_edge_risetime} shows the result of our +self-characterization experiments, where we used the frontend to measure its own pulse shape representing its concrete +sampling performance. In these experiments, we used $256\times$ oversampling at \qty{12}{b} ADC resolution. The plots +show the voltage at the ADC input against time in \unit{\nano\second}. The absolute voltage levels are not relevant here +- only the rise time is. Since we use some of these amplifiers--particularly the redriver ICs--well outside of their +intended application, the actual voltage they develop across the nonlinear load that our sampling gate's diode bridge +presents depends on implementation details of the amplifier's CML output stage. To maximize ADC resolution and minimize +ringing, we tuned gain and bandwidth of each post-sampling amplifier for each IC. Ringing in the amplifier output leads +to jitter in the ADC's sampling period to directly feeding through to the ADC output value. Since in \partno{STM32} +MCUs, the ADC is clocked independently of the rest of the system, its sampling timing is poorly +controlled and this jitter causes a significant error unless the amplifier is well-compensated. Table\ \ref{tab_edge_risetime} shows rise times calculated from each trace, averaged across both traces of the -differential pair. From these results and from the graphs in Figure\ \ref{fig_edge_risetime} we can see that in the -optical networking limiting amplifier produces slower edges than the measurements from Figure\ \ref{fig_spec_risetime} -would suggest. We suspect that this is caused by its low output amplitude resulting in part from its specifications and -in part from a poor match between its CML output structure and the nonlinear impedance presented by the sampling diode -bridges. Surprisingly, even the \partno{74LVC2G157} baseline unit has a rise time of less than \qty{1}{\nano\second}. We -estimate that this is caused by the large output voltage swing of this part, going from ground to its $V_{CC}$ at -\qty{3.3}{\volt}. Due to the construction of our sampling gate, its switching happens in the short period between its -input differential voltage crossing zero and it rising above the combined forward voltage of the Schottky diodes. Thus, -while the \partno{74LVC} might produce slow edges overall, its large output swing results in a high slew rate in the -critical region around the zero crossing that mostly determines the speed of the sampling gates. +differential pair. Our results show that the optical networking limiting amplifier produces slower edges than the +measurements from Figure\ \ref{fig_spec_risetime} would suggest. We suspect that this is caused by its low output +amplitude resulting in part from its specifications and in part from a poor match between its CML output structure and +the nonlinear impedance presented by the sampling diode bridges. Surprisingly, even the \partno{74LVC2G157} baseline +unit has a rise time of less than \qty{1}{\nano\second}. We estimate that this is caused by the large output voltage +swing of this part, going from ground to its $V_{CC}$ at \qty{3.3}{\volt}. Due to the construction of our sampling gate, +its switching happens in the short period between its input differential voltage crossing zero and it rising above the +combined forward voltage of the Schottky diodes. Thus, while the \partno{74LVC} might produce slow edges overall, its +large output swing results in a high slew rate in the critical region around the zero crossing. We observed the best result overall with the \partno{PI3HDX12211} redriver, resulting in a rise time of \qty{264}{\pico\second}. In this test specimen, we fed the pulse through the amplifier twice since we had two unused -channels, and we used \qty{200}{\pico\second} clip lines on the amplifier's output for pulse shaping. We could only use -the clip lines in this specimen as in all other specimens, the amplifiers' output did not contain sufficient harmonic -content such that it was still able to turn on the sampling gate's diode bridge when used with the clip line. +channels, and we used \qty{200}{\pico\second} clip lines on the amplifier's output for pulse shaping. We only used clip +lines here and for \partno{TDP0604} since the other amplifiers' output did not contain sufficient harmonic content. \subsection{Mesh Specimen Characterization} \begin{table} \begin{center} \begin{tabular}{r|cccc} - \textbf{Specimen} + \textbf{Mesh} &1 &2 &3 @@ -822,56 +811,53 @@ content such that it was still able to turn on the sampling gate's diode bridge \qty{26}{\nano\second}\\ \end{tabular} \end{center} - \caption{Specifications of mesh test specimens used in the experiments in this paper. All four specimens were placed - on a single, four-layer, \qty{1.0}{\milli\meter} thickness PCB. The meshes were placed two per side on the outer - layers, and the inner layers were used as ground. Approximate signal delays were calculated using wave velocity + \caption{Specifications of mesh test specimens used in the experiments in this paper. Approximate signal delays were + calculated using wave velocity $v=\frac{c}{\sqrt{\epsilon_r}}\approx\frac{c}{2}$\cite{wheelerTransmissionLinePropertiesParallel1965} assuming $\epsilon_r\approx 4$\cite{mumbyDielectricPropertiesFR41989} for the test specimens' \partno{FR-4} substrate.} \label{tab_mesh_spec} \end{table} -To measure the practical performance of our prototype, we created a set of security mesh test specimens. Four specimens -each cover the same area using four different mesh pitches using two, looped mesh traces according to the design -specifications listed in Table\ \ref{tab_mesh_spec}. The four specimens have a trace length ratio of approximately -$1:2:3:4$. As a baseline validation of our prototype as well as the mesh design, we performed TDR measurements of each -mesh specimen using each amplifier variant of our prototype. Figure\ \ref{fig_mesh_length} shows the results of these -measurements. The graphs show the step response resulting from an edge entering the mesh, and its reflection arriving -back at the start after traversing the mesh back and forth. +To measure the practical performance of our prototype, we created a set of tamper sensing mesh test specimens. Each +specimen contains four separate meshes with the same area. Table~\ref{tab_mesh_spec} shows the design specifications. +Each specimen contains four separate meshes on the outer layers of a four-layer, \qty{1.0}{\milli\meter} thickness PCB, +two equal-size meshes on each side. The inner layers were used as ground. Figure\ \ref{fig_mesh_length} shows the +results of a baseline measurement of each mesh using each design variant. The step response resulting from an edge +entering the mesh and its reflection arriving back at the start after traversing the mesh back and forth is clearly +visible. We validated the results from Figure\ \ref{fig_mesh_length} by calculating speed of light in our mesh specimen's substrate based on them. The resulting measurements are shown in Table\ \ref{tab_speed_of_light}. All amplifier -configurations yield comparable measurements of approximately \qty{1.6}{\meter\per\second}, which corresponds well with -the expected signal propagation velocity in \partno{FR-4} PCB material of +configurations yield comparable measurements of approximately \qty{1.6}{\meter\per\second}, which corresponds with the +expected signal propagation velocity in \partno{FR-4} PCB material of \qty{1.5d8}{\meter\per\second}\cite{wheelerTransmissionLinePropertiesParallel1965,mumbyDielectricPropertiesFR41989}. -An interesting aspect of the graphs in Figure\ \ref{fig_mesh_length} is that all except the \partno{74LVC} graph show a -dispersion effect increasingly rounding out the trailing edge of the response with longer mesh lengths. We suspect this -effect stems from higher-frequency components coupling into adjacent trace segments further up or down the mesh more -easily, spreading high-frequency components of the response signal out throughout time and effectively creating a -low-pass response. We suspect the poor visibility of this effect in the \partno{74LVC} measurements is a result of this -variant's pulse amplifier output amplitude being very large, allowing reflected response components to forward-bias the -sampling gate's diode bridges, resulting in amplitude clipping. +The graphs in Figure~\ref{fig_mesh_length} show a dispersion effect that increasingly rounds off the trailing edge of +the response with longer mesh lengths. This effect stems from higher-frequency components coupling into adjacent trace +segments further up or down the mesh, spreading high-frequency components of the response signal out throughout time. +This effect is less visible in the \partno{74LVC} measurements, which we suspect is a result of this variant's large +pulse amplitude, which enables reflected response components to forward-bias the sampling gate's diode bridges, +resulting in amplitude clipping. From this dispersion effect follows a key point for the design of practical security meshes: To increase the temporal -resolution of TDR mesh monitoring, meshes should be broken up into relatively short segments that are multiplexed -through signal switching. Where this is not desirable, the mesh can be treated as a microwave circuit design that can be -optimized through the electronic CAD/electromagnetic simulation co-design approach used for such circuits. +resolution of TDR mesh monitoring, meshes should be broken up into segments that are multiplexed through signal +switching. \begin{figure} \begin{center} \includegraphics[width=\textwidth]{fig_mesh_length.pdf} \vspace*{-10mm} \end{center} - \caption{TDR responses captured using our design with each of four candidate pulse amplifier ICs and four mesh test - specimens. The shown time range covers the primary reflection of the stimulus pulse's falling edge. The vertical - scale of all four graphs is in Volts at the ADC. For clarity, only one channel of the response is shown.} + \caption{TDR responses captured using our design with each of four candidate pulse amplifier ICs and four test + meshes. The shown time range covers the primary reflection of the stimulus pulse's falling edge. The vertical + scale of the graphs is in Volts at the ADC. For clarity, only one channel of the differential response is shown.} \label{fig_mesh_length} \end{figure} \begin{table} \begin{center} \begin{tabular}{r|cccc|c} - &\multicolumn{4}{c|}{Specimen}&\\ + &\multicolumn{4}{c|}{Mesh}&\\ Pulse amplifier IC& 1& 2& @@ -914,94 +900,201 @@ optimized through the electronic CAD/electromagnetic simulation co-design approa \label{tab_speed_of_light} \end{table} -\subsection{Tamper tests} +\color{highlightgreen} +\subsection{Classification performance} +\label{sec-class-perf} -After validating our prototype's electrical performance as well as our mesh specimen designs in the previous sections, -we performed a series of experiments where we performed tampering attempts on a mesh specimen while monitoring it using -our TDR prototype, capturing responses both before and after tampering. We performed two sets of experiments. +To evaluate the practical performance of our system in a baseline scenario, we captured approximately 1250 measurement +series under a variety of environmental and attack conditions. In each series, we captured 7 differential traces with +$2\times768$ points per trace. One differential trace served as a calibration reference with the multiplexers configured +to disconnect the mesh. The other six traces cover each of open circuit, short circuit, and matched load termination +measuring the mesh once from each of both ends. -\subsubsection{Short and Open Circuits} -\label{sec_attack_short} +We explored two variants of our baseline classifier, each consisting of three steps: First, traces are passed through a +B-spline smoothing filter. This filter serves as a low-pass filter, evening out noise contributions. We only applied +this filter where necessary. Second, we calculate a distance between each channel +($\{\text{open},\text{short},\text{load}\}\times\{\text{forward},\text{reverse}\}\times\{\text{positive},\text{negative}\}$ +of the baseline trace and the corresponding channel of the experiment traces, resulting in a vector with 12 entries. +Third, we apply a norm to this vector to reduce it to a single, scalar distance value. -\begin{figure} - \begin{center} - \includegraphics[width=\textwidth]{fig_manip_shape.pdf} - \end{center} - \caption{TDR responses captured using our design under three short- and one open-circuit scenario. The distance from - mesh start to Location 1, 2, and 3 is \qty{558}{\milli\meter}, \qty{125}{\milli\meter} and \qty{850}{\milli\meter}, - respectively. The cut is approximately halfway through the mesh. Left and right plots show the positive and negative - trace of the differential pair, respectively. Black traces show baseline measurements in between attacks. The - baselines show vertical offsets due to temperature drift, which causes a small DC offset in our design. The vertical - scale is in Volts at the ADC.} - \label{fig_manip_shape} -\end{figure} +The two variants of our classifier differ in the distance function and the vector norm. The first variant uses the +pearson ccorrelation coefficient as its distance function and mean as its vector norm. The second variant uses the +maximum distance at any one trace point as its distance function, and selects the maximum component in its vector norm. +The first variant is sensitive to changes in the overall shape of a trace, while the second variant is sensitive to +localized changes to one or a few points of a trace. -In our first experiment, we tested both short and open-circuit conditions. We tested a short circuit between the two -mesh traces in three locations as well as a cut trace halfway through the mesh. Figure\ \ref{fig_pic_specimens} in -Appendix\ \ref{appendix_photos} shows photos of our test specimens. Figure\ \ref{fig_manip_shape} shows the result of -our experiment. The graphs show a clear response of our monitoring circuit to all four tampering scenarios. Short and -open circuit conditions can clearly be distinguished from each other, and in all cases, the fault location can be -determined with sub-nanosecond precision, corresponding to several centimeters in distance along the mesh. +Figure~\ref{fig_layout_identity} shows the performance of the correlation classifier on intact meshes. For each +performance measurement, we show the correlation matrix between a set of baseline measurements and a set of experiment +measurements. High values indicate similarity, low values indicate differences. We show the baseline set top +left, and the experiment set bottom right. Uniform color within the top left indicates high similarity between baseline +measurements. Nonuniform color in the bottom right is expected, and indicates that mutliple experiment (attack) +measurements are unlike each other. Classification performance is indicated by the top right and bottom left quadrants, +which indicate misclassification probability. Misclassification is likely when the top left and top right quadrants look +alike. Misclassification is unlikely the more they differ. -\subsubsection{Probing by Oscilloscope Probe} -\label{sec_attack_probe} +Figure~\ref{fig_layout_identity_layout} compares several copies of the same mesh (top left) to four variants that have +the same pitch and area, but different layout of the traces (bottom right). Here and in all following graphs we list the +false negative / missed alarm rate of the classifier when calibrated to a $0.1\%$ false positive / false alarm rate +calculated assuming normally distributed samples as well as the crossover error rate calculated from the empirical +cumulative distribution function. In this instance, our classifier can clearly distinguish mesh layouts in most cases. -\begin{figure} - \begin{center} - \includegraphics[width=\textwidth]{fig_probe_shape.pdf} - \vspace*{-7mm} - \end{center} - \caption{The circuit's TDR response under a probing attack using an oscilloscope probe. Black traces are a series of - un-probed baseline measurements taken between attacks. All traces are plotted relative to a separate baseline trace - taken at the beginning of the experiment. The top and bottom plots show the two halves of the differential pair.} - \label{fig_probe_shape} -\end{figure} - -In our second experiment, we probed each of the three locations from the test specimen shown in Figure\ -\ref{fig_pic_specimens} in the Appendix once at each trace of the trace pair using a Rigol \partno{PVP3150} $\times -1/\times 10$ oscilloscope probe set to $\times 10$ mode. We grounded the probe's ground clip to the mesh ground and used -the probe without tip attachment. - -Using the \partno{PI3HDX12211} variant of our prototype, we measured the mesh's TDR response while probing. Figure\ -\ref{fig_manip_shape} shows the resulting TDR traces. Oscilloscope probes are specifically designed to disturb the -circuit under test as little as possible, with this one being specified as presenting as a \qty{10}{\mega\ohm} resistive -load in parallel with a \qty{10}{\pico\farad} capacitance when used in $\times 10$ mode as we did here. Since the -resulting disturbance to the TDR traces is smaller than those in Figure\ \ref{fig_manip_shape}, we post-processed the -traces by subtracting a baseline trace taken before the measurements. To highlight drift in the baseline trace, we -include additional baseline traces taken in between and after measurements using the same post-processing. - -In each trace, the mesh was probed in one of three locations as in Figure\ \ref{fig_manip_shape}, and on one of the two -mesh traces. The time range shown in the graph covers the primary reflection of the stimulus pulse's rising edge. We can -clearly see a distinct response to each of the three probing attempts with the only caveat being that the response of -the two mesh traces is asymmetrical due to asymmetry in our sampling frontend when measuring such low signal levels. -Interestingly, this asymmetry is fully compensated by the fact that we excite the mesh differentially, and as a result -probing either trace distorts their shared electromagnetic field, and impacts measurements on \emph{both} traces. -Particularly on the first trace, we can distinguish which trace was probed, as well as where it was probed, in a single -measurement. - -\subsubsection{Circumvention Through Micro-Soldering} -\label{sec_attack_bridge} +The variance between samples of the baseline group in Figure~\ref{fig_layout_identity_layout} alerted us to the +possibility that while all mesh samples of the same layout were supposed to be identical copies, our measurement circuit +might be sensitive enough to pick up on manufacturing variations from one copy to another in a PUF-like manner. To +evaluate this scenario, in Figure~\ref{fig_layout_identity_identity} we show the result of repeated measurements of +three copies of the same mesh. The measurements were taken interleavedi (i.e. $1, 2, 3, 1, 2, \hdots$) to exclude +systematic errors from affecting the conclusion. As we can see, our system indeed exhibits a PUF-like response and can +distinguish multiple copies of the same mesh with precision. We leave a detailed analysis of this effect to future work. +For the scope of this paper, the presense of this effect indicates good performance of our design, and increases the +detection efficiency of our approach. \begin{figure} \centering - \begin{subfigure}{0.78\textwidth} - \centering - \includegraphics[width=\textwidth]{fig_drill_mod_shape.pdf} - \label{fig_drill_mod_shape_plot} + \begin{subfigure}[t]{0.28\textwidth} + \includegraphics[width=\textwidth]{fig_covar_distinguish_layouts.pdf} + \caption{Different mesh layouts, False negative rate 18\% at 0.1\% false positive rate, CER=0\%} + \label{fig_layout_identity_layout} \end{subfigure} - \begin{subfigure}{0.2\textwidth} - \centering - \includegraphics[width=\textwidth]{pic_manip_microsoldering_small.jpg} - \vspace*{2mm} - \label{fig_drill_mod_shape_pic} + \hspace*{5mm} + \begin{subfigure}[t]{0.28\textwidth} + \includegraphics[width=\textwidth]{fig_covar_distinguish_copies_large_run.pdf} + \caption{Three identical copies, False negative rate 1.7\% at 0.1\% false positive rate, CER=0\%} + \label{fig_layout_identity_identity} \end{subfigure} - \caption{The circuit's TDR response under a manipulation attack bridging part of a trace to allow a - \qty{300}{\micro\meter} drill to penetrate. The mesh pitch is \qty{240}{\micro\meter}. Red traces show - measurements with a looped wire patch comparable to \textcite{immlerSecurePhysicalEnclosures2018}, black traces - show the same gap bridged with a minimally short straight piece of wire. The left and right plots show the two - halves of the differential pair. The photo shows the looped wire patch with a \qty{1}{\milli\meter} pitch ruler - for reference. Traces are normalized as in Figure\ \ref{fig_probe_shape}.} - \label{fig_drill_mod_shape} + \hfill + \caption{Measurements of intact meshes, correlation classifier.} + \label{fig_layout_identity} +\end{figure} + +\subsubsection{Basic attacks} + +\begin{figure} + \begin{subfigure}[t]{0.23\textwidth} + \includegraphics[width=\textwidth]{fig_covar_open_p0.3.pdf} + \caption{Open, p=\qty{0.3}{\milli\meter}. Missed alarm rate 0.0\% at 0.1\% false alarm rate, CER=0\%.} + \end{subfigure} + \hfill + \begin{subfigure}[t]{0.23\textwidth} + \includegraphics[width=\textwidth]{fig_covar_short_across_traces_p0.3.pdf} + \caption{Short, p=\qty{0.3}{\milli\meter}. Missed alarm rate 0.0\% at 0.1\% false alarm rate, CER=0\%.} + \end{subfigure} + \hfill + \begin{subfigure}[t]{0.23\textwidth} + \includegraphics[width=\textwidth]{fig_covar_open_p0.4.pdf} + \caption{Open, p=\qty{0.4}{\milli\meter}. Missed alarm rate 0.0\% at 0.1\% false alarm rate, CER=0\%.} + \end{subfigure} + \hfill + \begin{subfigure}[t]{0.23\textwidth} + \includegraphics[width=\textwidth]{fig_covar_short_across_traces_p0.4.pdf} + \caption{Short, p=\qty{0.4}{\milli\meter}. Missed alarm rate 0.0\% at 0.1\% false alarm rate, CER=0\%.} + \end{subfigure} + \caption{Covariance matrix of intact (top left) and modified meshes (bottom right). Shown are two pitches. Ten + specimens each with either one trace interrupted, or both traces shorted in a random location.} + \label{fig_covar_basic_attacks} +\end{figure} + +Figure~\ref{fig_covar_basic_attacks} shows the performance of our classifier under the two basic attack scenarios of an +interrupted trace, and a short between the mesh's differential traces. Such attacks lead to large changes in the +location of the reflected pulse edge, which our classifier picks up with perfect accuracy across our test set. + +\subsubsection{Hairpin shortening} + +\begin{figure} + \centering + \begin{subfigure}[t]{0.28\textwidth} + \includegraphics[width=\textwidth]{fig_covar_short_within_0.3.pdf} + \caption{Correlation classifier, False negative rate 18\% at 0.1\% false positive rate, CER=17\%} + \label{fig_short_within_corr} + \end{subfigure} + \hspace*{5mm} + \begin{subfigure}[t]{0.28\textwidth} + \includegraphics[width=\textwidth]{fig_covar_short_within_0.3_min_max.pdf} + \caption{Min/Max classifier, False negative rate 23\% at 0.1\% false positive rate, CER=23\%} + \label{fig_short_within_minmax} + \end{subfigure} + \hfill + \caption{Classification results of several mesh specimens that have one trace shorted to an adjacent location on the + same trace.} + \label{fig_short_within} +\end{figure} + +When one trace is not shorted to the other mesh trace, but instead shorted to another location within the same trace, +the resulting distortion in response shape is harder to detect. The reason for this is that such modifications introduce +a skew in the delay of the differential pair. Depending on the length of the shorted-out section, this skew may be as +little as a few picoseconds, which is hard to detect given our system's measurement resolution. + +Figure~\ref{fig_short_within} shows the performance of our classifier under this scenario. As we can see in the +structure of the correlation plots, for some samples which have longer sections of mesh trace shorted out, this attack +is easy to distinguish, but for others, where only a short section of trace is shorted out, it is harder to distinguish. + +\subsubsection{Advanced attacks} + +\begin{figure} + \begin{subfigure}[t]{0.23\textwidth} + \includegraphics[width=\textwidth]{fig_covar_probe_0.3.pdf} + \caption{Oscilloscope probe. Missed alarm rate 0.0\% at 0.1\% false alarm rate, CER=0\%.} + \label{fig_covar_adv_probe} + \end{subfigure} + \hfill + \begin{subfigure}[t]{0.23\textwidth} + \includegraphics[width=\textwidth]{fig_covar_soldering_p0.3.pdf} + \caption{Soldering iron. Missed alarm rage 0.0\% at 0.1\% false alarm rate, CER=0\%.} + \label{fig_covar_adv_soldering} + \end{subfigure} + \hfill + \begin{subfigure}[t]{0.23\textwidth} + \includegraphics[width=\textwidth]{fig_covar_antenna_wire_30mm_p0.3.pdf} + \caption{30mm wire soldered. Missed alarm rage 9.6\% at 0.1\% false alarm rate, CER=1\%.} + \label{fig_covar_adv_antenna} + \end{subfigure} + \hfill + \begin{subfigure}[t]{0.23\textwidth} + \includegraphics[width=\textwidth]{fig_covar_probe_points_p0.3.pdf} + \caption{Baseline vs. specimens with soldermask removed for previous plots.} + \label{fig_covar_adv_baseline} + \end{subfigure} + \caption{} + \label{fig_covar_adv_attack} + %too much: fig_covar_soldering_p0.3_minmax.pdf + %too much: fig_covar_antenna_wire_30mm_p0.3_minmax.pdf +\end{figure} + +Figure~\ref{fig_covar_adv_attack} shows our classifier's performance under a set of more advanced attacks: An +oscilloscsope probe touching one mesh trace (Figure~\ref{fig_covar_adv_probe}, Rigol PVP3150 probe), a soldering iron +touching one mesh trace (Figure~\ref{fig_covar_adv_soldering}), and a mesh where one trace has a +$l=\qty{30}{\milli\meter},d=\qty{120}{\micro\meter}$ copper wire soldered to one trace +(Figure~\ref{fig_covar_adv_probe}). The probing attack is interesting since oscilloscope probes are specifically +designed to disturb the probed circuit as little as possible. The wire attack simulates an attacker attaching a wire in +an attempt to patch a trace in preparation for an attack. Our classifier is able to clearly distinguish each attack. +Figure~\ref{fig_covar_adv_baseline} compares baseline specimens against the three specimens that had soldermask removed +for these attacks while no attack is being conducted. This result shows that this preparation has no effect on the +measurement. + +\subsubsection{Patching attacks} +\label{sec_attack_probe} + +\begin{figure} + \begin{subfigure}[t]{0.27\textwidth} + \includegraphics[width=\textwidth]{fig_covar_patch_interleave_baseline.pdf} + \caption{Test boards before experiment} + \label{fig_covar_patch_attack_baseline} + \end{subfigure} + \hfill + \begin{subfigure}[t]{0.27\textwidth} + \includegraphics[width=\textwidth]{fig_covar_patch_ref_exp_interleave_direct.pdf} + \caption{Experiment specimen compared to reference before and after} + \label{fig_covar_patch_attack_direct} + \end{subfigure} + \hfill + \begin{subfigure}[t]{0.4\textwidth} + \includegraphics[width=\textwidth]{fig_patch_interleave_scatter.pdf} + \caption{Trajectory of experiment and control speciments} + \label{fig_covar_patch_attack_scatter} + \end{subfigure} + \hfill + \caption{Classifier performance under a patching attack that bridges a short gap within a mesh trace using wire. + B-spline smoothing was applied during classification.} + \label{fig_covar_patch_attack} \end{figure} While our proposed measurement setup significantly increases the level of effort required from an attacker, as long as @@ -1010,18 +1103,158 @@ for PCB repair. If we assume a standard PCB process with \qty{100}{\micro\meter} attack targeting a \qty{300}{\micro\meter} hole size as proposed by \textcite{immlerSecurePhysicalEnclosures2018} will break at least one trace. Patching the resulting break using a wire is possible, but with increasing wire length, the TDR response of the mesh is increasingly distorted. We experimentally performed an attack comparable to the one shown by -\textcite{immlerSecurePhysicalEnclosures2018} on a \qty{240}{\micro\meter} pitch mesh specimen. Figure\ -\ref{fig_drill_mod_shape} shows our modification and the resulting change in TDR response. As we can see, adding even -just a few millimeters of wire will measurably and consistently distort the TDR response. +\textcite{immlerSecurePhysicalEnclosures2018} on a \qty{300}{\micro\meter} pitch mesh specimen. In this attack, we +removed a small part of one mesh trace and bridged it with a wire. Figure\ \ref{fig_drill_mod_shape} shows our +modification and the resulting change in the time-domain response. + +Figure~\ref{fig_covar_patch_attack} shows the classification result of this attack. Because the patch is small, +this type of attack leaves only subtle traces in the measurement data. To extract this effect, we performed two +experiments in a row. First, we interleaved measurements of two reference specimens, a control specimen, and the +unmodified experiment specimen to establish a baseline. Then, we modified the experiment specimen and repeated the +experiment. Temperature drift and other possible external factors affecting the measurement can be excluded by comparing +both control and experiment measurements against the two references before and after the modification. +Figure~\ref{fig_covar_patch_attack_baseline} shows the four samples before the attack, exhibiting the same subtle +PUF-like effect that we described in Section~\ref{sec-class-perf}. Since we peform both before and after measurements on +the same sample, we can separate this effect from the effect of the attack. Figure~\ref{fig_covar_patch_attack_direct} +compares both control and experiment samples before and after the attack, and shows a clear change in the experiment +sample during the attack. Figure~\ref{fig_covar_patch_attack_scatter} plots the similarity of both samples to each of +the two reference samples. We can see that the control distribution stays in one place, while the experiment +distribution shifts. + +\begin{figure} + \centering + \begin{subfigure}{0.78\textwidth} + \centering + \includegraphics[width=\textwidth]{fig_drill_mod_shape_new.pdf} + \label{fig_drill_mod_shape_plot} + \end{subfigure} + \begin{subfigure}{0.2\textwidth} + \centering + \includegraphics[width=\textwidth]{pic_manip_microsoldering_new_small.jpg} + \vspace*{2mm} + \label{fig_drill_mod_shape_pic} + \end{subfigure} + \caption{The mesh response under a manipulation attack patching across a drill location for a + \qty{300}{\micro\meter} drill. The mesh pitch is \qty{300}{\micro\meter}. Traces were smoothed for readability.} + \label{fig_drill_mod_shape} +\end{figure} + +Based on the above results, we peformed a larger-scale experiment using seven samples with patches applied compared +against baseline measurements taken before and after measuring the experiment samples. Each sample was measured ten +times in an interleaved order. Figure~\ref{fig_patch_large_scale} shows the results of this experiment. As we can see, +the min/max classifier is better at distinguishing the subtle, localized effects of such patches. Using the min/max +classifier, half of attack attempts are detected in a single measurement when fixing the false alarm rate at 0.1\%. + +\begin{figure} + \centering + \begin{subfigure}{0.3\textwidth} + \centering + \includegraphics[width=\textwidth]{fig_covar_patch_repeat_p0.3.pdf} + \caption{Correlation classifier. Missed alarm rate 71.5\% at 0.1\% false alarm rate, CER=34\%.} + \label{fig_patch_large_scale_corr} + \end{subfigure} + \begin{subfigure}{0.3\textwidth} + \centering + \includegraphics[width=\textwidth]{fig_covar_patch_repeat_p0.3_minmax.pdf} + \caption{Min/max classifier. Missed alarm rate 51.1\% at 0.1\% false alarm rate, CER=15\%.} + \label{fig_patch_large_scale_minmax} + \end{subfigure} + \caption{Classification performance in a larger-scale experiment using 10 measurements each of 7 samples with + traces patched through micro-soldering. B-spline smoothing was applied before classification.} + \label{fig_patch_large_scale} +\end{figure} + +\subsubsection{Environmental susceptibility} + +The measurement sensitivity of our design raises the question of how environmental factors such as handling, or +electromagnetic interference affect the measurements. Figure~\ref{fig_env_effects} shows the result in several +scenarios. As shown in Figure~\ref{fig_env_effects_time}, time alone does not contribute significantly to the +measurement results. As indicated by Figure~\ref{fig_env_effects_touch}, touching parts of the device other than the +mesh during normal handling also does not disturb measurements. However, when the mesh is directly touched, this can +easily be detected. In a practical application, this is of little concern since any PCB tamper sensing mesh would lie on +the inside of the device. Since the meshes we use have a continous ground plane, a simple solution to touch sensitivity +is to put the ground plane on the outside of the device, shielding the mesh from touching. + +A significant effect on the measurements can be seen when the mesh is heated, as shown by the results in +Figure~\ref{fig_env_effects_heat}. Figure\ \ref{fig_tempco_time} shows the relative difference between the time-domain +response of a mesh at room temperature and a mesh heated to \qty{70}{\degree C}. This temperature dependence has two +main factors. First, the resistance of the mesh's copper traces has a positive temperature coefficient, meaning that its +resistance increases with temperature. Across the \qty{50}{\degree C} temperature difference shown here, this +corresponds to a change in resistance of approximately 20\%. Besides the resistance of copper, the dielectric constant +and dissipation factor of the FR-4 dielectric of the mesh PCB also have a significant temperature +coefficient\cite{sagarStudiesTemperatureDependent2024,hinagaThermalEffectsPCB2010}. An increase in copper resistance can +be seen in the overall shift of the response curve due to resistive attenuation. An increase in the dielectric +dissipation factor can be seen in the slope of the difference, since pulse energy is dissipated more the longer the +pulse travels through the material. Finally, a change in dielectric constant moves the response's trailing edge in time, +with the pulse propagating slightly slower at high temperature. + +Since these effects are consistent with physical predictions and only reach problematic levels at large temperature +differences, it would be possible to design a classifier that is insensitive to temperature effects. Furthermore, given +the predictable, physical nature of these effects, they could also be compensated before classification in the digital +domain based on a temperature measurement and a set of per-mesh calibration data. + +\begin{figure} + \begin{subfigure}[t]{0.25\textwidth} + \includegraphics[width=\textwidth]{fig_covar_time_drift.pdf} + \caption{Time drift (2.5h). False negative rate 100\% at 0.1\% false positive rate, CER=60\%.} + \label{fig_env_effects_time} + \end{subfigure} + \hfill + \begin{subfigure}[t]{0.4\textwidth} + \includegraphics[width=\textwidth]{fig_covar_touch_combined.pdf} + \caption{Touch sensitivity. False negative rate 0.0\% at 0.1\% false positive rate, CER=0\%.} + \label{fig_env_effects_touch} + \end{subfigure} + \hfill + \begin{subfigure}[t]{0.25\textwidth} + \includegraphics[width=\textwidth]{fig_covar_hot_mesh.pdf} + \caption{Mesh heated (\qty{70}{\degree C}). False negative rate 0.6\% at 0.1\% false positive rate, CER=0\%.} + \label{fig_env_effects_heat} + \end{subfigure} + \caption{Classification results of the same mesh under various environmental factors.} + \label{fig_env_effects} +\end{figure} + +\begin{figure} + \centering + \includegraphics[width=1.0\textwidth]{fig_tempco_edited.pdf} + \caption{The effect of heating on a time-domain trace. One of 12 channels shown. Gray: Raw data. Black: Relative + difference between hot and cool cases.} + \label{fig_tempco_time} +\end{figure} + +Besides temperature, other environmental factors such as electromagnetic interference could theoretically also influence +our measurements. Although our system's equivalent-time sampling setup inherently cancels out EMI since it is not +synchronous to the sampling clock, the setup is unshielded so we verified its actual susceptibility in several +scenarios. Figure~\ref{fig_env_covar} shows the result of these measurement series. For comparison, we included several +measurements from Figure~\ref{fig_patch_large_scale}. From these figures, we can see that there are some environmental +effects, but these effects are small even when compared against a subtle attack like a patching attack. + +\begin{figure} + \begin{subfigure}{0.3\textwidth} + % NOTE: not actually "tridelta" data, I'm just too lazy to rename these and fix up the notebook. + \includegraphics[width=\textwidth]{fig_covar_patch_repeat_tridelta_all_the_data_p0.3.pdf} + \caption{Covariance Metric, Missed alarm rate 69.0\% at 0.1\% false alarm rate, CER=20\%.} + \end{subfigure} + \hspace*{2mm} + \begin{subfigure}{0.3\textwidth} + % NOTE: not actually "tridelta" data, I'm just too lazy to rename these and fix up the notebook. + \includegraphics[width=\textwidth]{fig_covar_patch_repeat_tridalta_all_the_data_p0.3_minmax.pdf} + \caption{Min/Max Metric, Missed alarm rate 63.5\% at 0.1\% false alarm rate, CER=17\%.} + \end{subfigure} + \caption{Covariance matrices comparing all environmental runs. For scale, measurements from + Figure~\ref{fig_patch_large_scale} are included on the bottom/right. B-spline smoothing was applied.} + \label{fig_env_covar} +\end{figure} \subsection{Countermeasures} -As shown above, PCB security meshes can be manipulated using industry-standard micro-soldering techniques. Keeping the -length of any patch wires as short as possible, it is conceivable that the impact on TDR response could be kept below -detection thresholds. Our setup provides increased resistance against such attacks since the entire attack would have to -be carried out without electrically contacting either mesh trace. In particular, soldering would have to be done using a -minimal amount of solder as well as a bespoke, insulated soldering iron tip. While manufacturing such a tool out of a -material like sintered ceramic is conceivable, to our knowledge, no such tool exists on the market. +As shown above, PCB security meshes can be manipulated through micro-soldering. Keeping the modifications as physically +small as possible, their impact on TDR response can potentially be kept below detection thresholds of our single-shot +baseline classifier. However, even with such a simple classifier, the entire attack would have to be carried out without +raising an alarm, e.g. by touching the mesh or contacting a trace with the soldering iron. Soldering would have to be +done using a minimal amount of solder as well as a bespoke, insulated soldering iron tip. While manufacturing such a +tool out of a material like sintered ceramic is conceivable, to our knowledge, no such tool exists on the market. Furthermore, the actual drilling would have to happen with a dielectric drill bit, placing special attention on evacuating conductive copper chips before they can create shorts to nearby traces. Again, it is conceivable that such a @@ -1029,183 +1262,42 @@ tool could be manufactured, but to our knowledge, such a tool is not currently a market. Finally, any probes penetrating the mesh would have to be placed such that their presence in the vicinity of the mesh -traces does not disturb the TDR response. In particular, we have observed that even touching the mesh will distort the -response, so modifications would have to be carried out with great care, likely using micromanipulators or similar -specialized equipment. +traces does not disturb the TDR response. Modifications would have to be carried out with great care, likely using +micromanipulators or similar specialized equipment. The PCI PTS HSM DTR standard\cite{pcisecuritystandardscouncilPaymentCardIndustry2021a} contains a useful framework for thinking about attacker capabilities. Applying their taxonomy, our monitoring system raises the skill level required for a patching attack from a \emph{skilled} attacker to an \emph{expert} attacker, and the equipment requirement from \emph{standard} equipment to \emph{bespoke} equipment such as dielectric drill bits and ceramic soldering tips. -% FIXME peer review only, for major revision @ TCHES -\color{highlightgreen} -\begin{figure}[H] - \begin{subfigure}{0.5\textwidth} - \includegraphics[width=\textwidth]{fig_covar_patch_repeat_tridelta_all_the_data_p0.3.pdf} - \label{fig_covar_patch_repeat_tridalta_all_the_data_covar} - \caption{Covariance Metric, Missed alarm rate 35.5\% at 0.1\% false alarm rate, CER=14.6\%.} - \end{subfigure} - \hspace*{2mm} - \begin{subfigure}{0.5\textwidth} - \includegraphics[width=\textwidth]{fig_covar_patch_repeat_tridalta_all_the_data_p0.3_minmax.pdf} - \label{fig_covar_patch_repeat_tridalta_all_the_data_minmax} - \caption{Min/Max Metric, Missed alarm rate 91\% at 0.1\% false alarm rate, CER=22.6\%.} - \end{subfigure} - \caption{Covariance matrices comparing all environmental runs as well as experiment baselines and seven runs of - meshes that have a broken trace patched by a soldered wire.} -\end{figure} - -\begin{figure}[H] - \begin{subfigure}[t]{0.23\textwidth} - \includegraphics[width=\textwidth]{fig_covar_probe_0.3.pdf} - \label{} - \caption{Oscilloscope probe} - \end{subfigure} - \hfill - \begin{subfigure}[t]{0.23\textwidth} - \includegraphics[width=\textwidth]{fig_covar_soldering_p0.3.pdf} - \label{} - \caption{Soldering iron} - \end{subfigure} - \hfill - \begin{subfigure}[t]{0.23\textwidth} - \includegraphics[width=\textwidth]{fig_covar_antenna_wire_30mm_p0.3.pdf} - \label{} - \caption{30mm wire soldered} - \end{subfigure} - \hfill - \begin{subfigure}[t]{0.23\textwidth} - \includegraphics[width=\textwidth]{fig_covar_probe_points_p0.3.pdf} - \label{} - \caption{Baseline vs. specimens with soldermask removed for previous plots} - \end{subfigure} - \caption{} - %too much: fig_covar_soldering_p0.3_minmax.pdf - %too much: fig_covar_antenna_wire_30mm_p0.3_minmax.pdf -\end{figure} - -\begin{figure}[H] - \begin{subfigure}[t]{0.25\textwidth} - \includegraphics[width=\textwidth]{fig_covar_time_drift.pdf} - \label{} - \caption{Time drift (2.5h)} - \end{subfigure} - \hfill - \begin{subfigure}[t]{0.4\textwidth} - \includegraphics[width=\textwidth]{fig_covar_touch_combined.pdf} - \label{} - \caption{Touch sensitivity} - \end{subfigure} - \hfill - \begin{subfigure}[t]{0.25\textwidth} - \includegraphics[width=\textwidth]{fig_covar_hot_mesh.pdf} - \label{} - \caption{Mesh heated (\qty{70}{\degree C})} - \end{subfigure} - \caption{} - \label{} -\end{figure} - -\begin{figure}[H] - \centering - \includegraphics[width=1.0\textwidth]{fig_tempco_edited.pdf} - \caption{The effect of heating on a time-domain trace. One of 12 channels shown. Gray: Raw data. Black: Relative - difference between hot and cool cases.} - \label{fig_pic_board} -\end{figure} - -\begin{figure}[H] - \begin{subfigure}[t]{0.27\textwidth} - \includegraphics[width=\textwidth]{fig_covar_patch_interleave_baseline.pdf} - \label{Test boards before experiment} - \caption{} - \end{subfigure} - \hfill - \begin{subfigure}[t]{0.27\textwidth} - \includegraphics[width=\textwidth]{fig_covar_patch_ref_exp_interleave_direct.pdf} - \label{} - \caption{Experiment specimen compared to reference before and after} - \end{subfigure} - \hfill - \begin{subfigure}[t]{0.4\textwidth} - \includegraphics[width=\textwidth]{fig_patch_interleave_scatter.pdf} - \label{} - \caption{Trajectory of experiment and control speciments} - \end{subfigure} - \hfill - \caption{} - \label{} -\end{figure} % fig_covar_short_within_0.3.pdf % FIXME repeat these runs, we have conflicting data. Do runs in both .3 and .4, .4 % seems to work better. -\begin{figure}[H] - \begin{subfigure}[t]{0.23\textwidth} - \includegraphics[width=\textwidth]{fig_covar_open_p0.3.pdf} - \label{} - \caption{Open, p=\qty{0.3}{\milli\meter}} - \end{subfigure} - \hfill - \begin{subfigure}[t]{0.23\textwidth} - \includegraphics[width=\textwidth]{fig_covar_short_across_traces_p0.3.pdf} - \label{} - \caption{Short, p=\qty{0.3}{\milli\meter}} - \end{subfigure} - \hfill - \begin{subfigure}[t]{0.23\textwidth} - \includegraphics[width=\textwidth]{fig_covar_open_p0.4.pdf} - \label{} - \caption{Open, p=\qty{0.4}{\milli\meter}} - \end{subfigure} - \hfill - \begin{subfigure}[t]{0.23\textwidth} - \includegraphics[width=\textwidth]{fig_covar_short_across_traces_p0.4.pdf} - \label{} - \caption{Short, p=\qty{0.4}{\milli\meter}} - \end{subfigure} - \caption{Covariance matrix of intact (top left) and modified meshes (bottom right). Shown are two pitches. Ten - specimens each with either one trace interrupted, or both traces shorted in a random location.} - \label{} -\end{figure} - -\begin{figure}[H] - \centering - \begin{subfigure}[t]{0.28\textwidth} - \includegraphics[width=\textwidth]{fig_covar_distinguish_layouts.pdf} - \label{} - \caption{Different mesh layouts, False negative rate 18\% at 0.1\% false positive rate, CER=0\%} - \end{subfigure} - \hspace*{5mm} - \begin{subfigure}[t]{0.28\textwidth} - \includegraphics[width=\textwidth]{fig_covar_distinguish_copies_large_run.pdf} - \label{} - \caption{Three identical copies, False negative rate 1.7\% at 0.1\% false positive rate, CER=0\%} - \end{subfigure} - \hfill - \caption{} - \label{} -\end{figure} - % FIXME peer review only, for major revision @ TCHES -\color{black} +\color{highlightred} \section{Future Work} -\paragraph{Design variants.} We found that the timing jitter of our sampling frontend is low enough to reach the -\qty{184}{\pico\second} resolution limit of the \partno{STM32G4} \partno{HRTIM} peripheral. In our prototype, we -implemented a -- so far unused -- adjustable power supply for the \partno{74LVC} series buffer in between the -\partno{HRTIM} outputs and the pulse amplifier. By adjusting this buffer's power supply through one of the -microcontroller's digital-to-analog converter (DAC) channels, we expect that it should be possible to exploit the supply -voltage dependency of the propagation delay of \partno{74LVC} series CMOS logic to create a digitally controllable delay -with picosecond resolution. +%\paragraph{Design variants.} We found that the timing jitter of our sampling frontend is low enough to reach the +%\qty{184}{\pico\second} resolution limit of the \partno{STM32G4} \partno{HRTIM} peripheral. In our prototype, we +%implemented a -- so far unused -- adjustable power supply for the \partno{74LVC} series buffer in between the +%\partno{HRTIM} outputs and the pulse amplifier. By adjusting this buffer's power supply through one of the +%microcontroller's digital-to-analog converter (DAC) channels, we expect that it should be possible to exploit the supply +%voltage dependency of the propagation delay of \partno{74LVC} series CMOS logic to create a digitally controllable delay +%with picosecond resolution. -\paragraph{Non-sequential sampling.} Not all parts of the reflected signal are equally sensitive to tampering atttempts. -For instance, the reflection's trailing edge corresponds contains information on both the length of the mesh and on its -attenuation. Instead of recording the response waveform in a linear scan, in a practical application, more relevant -parts of the response such as this trailing edge could be scanned at a higher rate than other, less relevant parts. -Similarly, fast scans at a coarse time resolution could be interleaved with slow scans at a finer time resolution to -detect large changes more quickly. +%\paragraph{Non-sequential sampling.} Not all parts of the reflected signal are equally sensitive to tampering atttempts. +%For instance, the reflection's trailing edge corresponds contains information on both the length of the mesh and on its +%attenuation. Instead of recording the response waveform in a linear scan, in a practical application, more relevant +%parts of the response such as this trailing edge could be scanned at a higher rate than other, less relevant parts. +%Similarly, fast scans at a coarse time resolution could be interleaved with slow scans at a finer time resolution to +%detect large changes more quickly. +\color{highlightgreen} +\paragraph{Advanced attack classification.} While we proposed a simple baseline classifier, there is a large parameter +space for more advanced designs. For instance, a classifier could apply machine learning techniques to adapt to the +response of a particular mesh, learn its benigh behavior under temperature changes, and dynamically schedule sample +timing to focus attention on the parts of the response signal that are most susceptible to attacks. +\color{highlightred} \paragraph{Auxiliary applications.} The low-cost, embedded TDR frontend presented in this paper could be used for other monitoring tasks from tamper sensing to system health monitoring. For instance, \textcite{vaiSecureArchitectureEmbedded2015} propose checking the integrity of a PCBA using an external Vector Network @@ -1213,6 +1305,14 @@ Analyzer (VNA) attached to test points on the PCBA's Power Distribution Network similar to a VNA and it would be interesting to measure parts of the secure subsystem other than its security mesh using our TDR frontend. +\color{highlightgreen} +\paragraph{Characterization of PUF-like effects.} In Section~\ref{sec-class-perf}, we have described a PUF-like effect +we observed during measurements, where our baseline classifier was repeatedly able to distinguish supposedly identical +copies of the same mesh. It would be interesting to precisely characterize this effect and its dependence on factors +such as the chosen PCB manufacturer, and to quantify if it indeed rises to the level of a PUF in entropy and +repeatability. + +\color{black} \section{Conclusion} In this paper, we presented a design for a low-cost frontend for integrity monitoring of security meshes in applications @@ -1222,8 +1322,7 @@ TDR sampling. Our design creates a detailed fingerprint of the intact mesh's con of the mesh's traces but also reflects the impedance at every point along the mesh. Beyond simply detecting faults or manipulations that disturb the mesh without causing breaks, we have demonstrated our -prototype circuit's capability to distinguish and physically localize faults inside the mesh in several practical attack -scenarios with even careful attacks causing strong disturbances in the generated fingerprint. +prototype circuit's capability to reliably detect almost all of a wide range of practical attacks. Compared to the state of the art, our approach enables the monitoring of larger meshes, at higher sensitivity and lower cost. Our is easy to replicate, does not require any specialized or custom components, and unlocks high-security diff --git a/paper/pic_manip_microsoldering_new.jpg b/paper/pic_manip_microsoldering_new.jpg new file mode 100644 index 0000000..6fe9079 Binary files /dev/null and b/paper/pic_manip_microsoldering_new.jpg differ diff --git a/paper/pic_manip_microsoldering_new_small.jpg b/paper/pic_manip_microsoldering_new_small.jpg new file mode 100644 index 0000000..dd6e3c8 Binary files /dev/null and b/paper/pic_manip_microsoldering_new_small.jpg differ