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@ -386,12 +386,12 @@ fingerprinting technique aimed at detecting Hardware Trojans (HT) inserted into
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fujimotoDemonstrationHTDetectionMethod2018,
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fujimotoDemonstrationHTDetectionMethod2018,
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mosavirikImpedanceVerifOnChipImpedance2022}.
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mosavirikImpedanceVerifOnChipImpedance2022}.
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Usually, all chips on a board are directly connected to the board's PDN. Thus, characterizing the board's PDN does not
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Usually, all chips on a board are directly connected to the board's PDN. Thus, characterizing the board's PDN does not
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only yield information on possible modifications to the board's PDN itself such as modified traces or removed passive
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only yield information on possible modifications to the board's PDN itself---such as modified traces or removed passive
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components such as capacitors, it also reflects information about the internal structure of any chips or other
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components---it also reflects information about the internal structure of chips connected to the PDN. Impedance analysis
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components connected to the PDN. Impedance analysis techniques generally probe the circuit during operation using
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techniques generally probe the circuit during operation using high-frequency signals. They have been proven using an
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high-frequency signals. They have been proven using an external Vector Network Analyzer in
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external Vector Network Analyzer in one-Port~\cite{mosavirikSiliconEchoesNonInvasive2023} configuration measuring
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one-Port~\cite{mosavirikSiliconEchoesNonInvasive2023} configuration measuring reflected signal components as well as
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reflected signal components as well as using two or more ports measuring transmitted signal
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using two or more ports measuring transmitted signal components~\cite{zhuPDNPulseSensingPCB2023}. Both Time Domain
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components~\cite{zhuPDNPulseSensingPCB2023}. Both Time Domain
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Reflectometry~\cite{fujimotoDemonstrationHTDetectionMethod2018} and conventional frequency-domain VNA
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Reflectometry~\cite{fujimotoDemonstrationHTDetectionMethod2018} and conventional frequency-domain VNA
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measurements~\cite{mosavirikImpedanceVerifOnChipImpedance2022} have been shown to be effective. From a signal theory
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measurements~\cite{mosavirikImpedanceVerifOnChipImpedance2022} have been shown to be effective. From a signal theory
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point of view, both techniques can be considered equivalent.
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point of view, both techniques can be considered equivalent.
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@ -413,16 +413,15 @@ preparation of a target chip for backside attacks using onboard measurements~\ci
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that adapt the technique as an offensive tool for side-channel analysis (SCA)
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that adapt the technique as an offensive tool for side-channel analysis (SCA)
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attacks~\cite{monfaredLeakyOhmSecretBits2023}.
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attacks~\cite{monfaredLeakyOhmSecretBits2023}.
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The technique we propose in this work is related in that it also embeds a RF measurement circuit in a target board, and
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Similar to PDN impedance analysis, our proposed technique also embeds a RF measurement circuit in a target board. TDR
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that TDR and frequency-domain VNA measurements resolve the same information about a target circuit from a signal theory
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and frequency-domain VNA measurements resolve the same information about a target circuit from a signal theory
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perspective. Our system differs from the PDN impedance analysis literature in that it reaches a significantly higher
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perspective. Our system reaches a significantly higher bandwidth than embedded measurement setups from differs from PDN
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bandwidth than other embedded measurement setups, and that our proposed tamper-sensing meshes are specifically built as
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impedance analysis literature, and that our proposed tamper-sensing meshes are specifically built as sensors. Our
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sensors. Our technique is better suited to active tamper-sensing applications where the sensing circuit is continuously
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technique is better suited to active tamper-sensing applications where the sensing circuit is continuously powered. In
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powered, since in contrast to PDN impedance analysis techniques that need the entire PDN to be powered, our proposed
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contrast to PDN impedance analysis techniques that need the entire PDN to be powered, our proposed technique can be
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technique can be applied to protect an unpowered payload circuit. In a practical application, both PDN impedance
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applied to protect an unpowered payload circuit. In a practical application, both PDN impedance analysis and TDR-based
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analysis and TDR-based tamper-sensing meshes could complement each other to form a comprehensive defense where PDN
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tamper-sensing meshes could complement each other to form a comprehensive defense where PDN impedance analysis checks
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impedance analysis checks the core system's integrity, with TDR-based meshes covering everything outside the purview of
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the core system's integrity, with TDR-based meshes covering everything outside the purview of PDN impedance analysis.
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PDN impedance analysis.
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\color{black}
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\color{black}
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@ -477,10 +476,10 @@ multiplexers.
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\label{sec_system_design}
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\label{sec_system_design}
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A typical system design for an HSM with TDR-based tamper sensing meshes would consist of a PCB assembly containing
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A typical system design for an HSM with TDR-based tamper sensing meshes would consist of a PCB assembly containing
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payload components as well as the mesh monitoring circuit, and enclosed from all directions in rigid or flexible tamper
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payload components as well as the mesh monitoring circuit. Tamper-sensing meshes made from rigid or flexible PCBs would
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sensing mesh PCBs. In this paper we propose meshes that have a ground plane, which would be on the outer side of the
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enclose this PCB assembly from all directions. In this paper we propose meshes that have a ground plane, which would be
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mesh PCBs and shield electromagnetic interference from outside. Mesh monitoring would be battery powered and would
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on the outer side of the mesh PCBs and shield the system against electromagnetic interference. Mesh monitoring would be
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periodically check for tamper attempts.
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battery powered and would periodically check for tamper attempts.
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% FIXME cite IHSM paper
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% FIXME cite IHSM paper
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@ -488,8 +487,8 @@ We consider an attacker motivated to extract the payload's secrets. Self-destruc
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as tamper response against this type of attacker. Such an attacker might want to probe parts of the payload circuit
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as tamper response against this type of attacker. Such an attacker might want to probe parts of the payload circuit
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using either conventional electrical contacts or using electromagnetic near-field probes that must be placed right on
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using either conventional electrical contacts or using electromagnetic near-field probes that must be placed right on
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top of the feature to be probed. An attacker might further attempt to manipulate the payload circuit, such as by
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top of the feature to be probed. An attacker might further attempt to manipulate the payload circuit, such as by
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removing capacitors to enable a later power sidechannel attack. In preparation for an optical fault-injection attack, an
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removing capacitors to enable a later power side-channel attack. In preparation for an optical fault-injection attack,
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attacker might attempt decapsulating some of the payload circuit's ICs either using laser ablation or using chemical
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an attacker might attempt decapsulating some of the payload circuit's ICs either using laser ablation or using chemical
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etching. An attacker might also attempt fault injection attacks using either electrical contacts or electromagnetic
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etching. An attacker might also attempt fault injection attacks using either electrical contacts or electromagnetic
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fault injection probes near a target feature.
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fault injection probes near a target feature.
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@ -1038,16 +1037,16 @@ Classification performance is indicated by the top right (2) and bottom left (3)
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misclassification probability. Misclassification is likely when the top left (1) and top right (2) quadrants look alike.
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misclassification probability. Misclassification is likely when the top left (1) and top right (2) quadrants look alike.
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Misclassification is less likely the more they differ.
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Misclassification is less likely the more they differ.
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\color{highlightgreen}
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\color{highlightgreen}
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Under each figure, we give the False Negative Rate (FNR), i.e. the rate of missed alarms, when the threshold is adjusted
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Under each figure, we give the False Negative Rate (FNR) when the threshold is adjusted for a False Positive Rate (FPR)
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for a False Positive Rate, i.e. a false alarm rate, of $0.1\%$ as a reference point. We also provide the Crossover Error
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of $0.1\%$ as a reference point\footnote{We denote the rate of missed alarms as FNR and the false alarm rate as FPR.}.
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Rate (CER) at which for some threshold FPR is equal to FNR. We calculate all error rates assuming the similarity scores
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We also provide the Crossover Error Rate (CER) at which for some threshold FPR is equal to FNR. We calculate all error
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are normally distributed. We chose a reference point of $0.1\%$ FPR since it allows for a meaningful comparison based on
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rates assuming the similarity scores are normally distributed. We chose a reference point of $0.1\%$ FPR since it allows
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the hundreds of measurements our data is based on. In a practical application, the end-to-end FPR of the alarm system
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for a meaningful comparison based on the hundreds of measurements our data is based on. In a practical application, the
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would need to be significantly lower, probably in the range from $10^{-12}$ to $10^{-9}$ for a Mean Time Between Failures
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end-to-end FPR of the alarm system would need to be significantly lower, probably in the range from $10^{-12}$ to
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(MTBF) of several years. A practical system would likely include additional components filtering the output of our
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$10^{-9}$ for a Mean Time Between Failures (MTBF) of several years. A practical system would likely include additional
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proposed baseline classifier analyzing not just the last, but multiple previous measurements. Experimentally evaluating
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components filtering the output of our proposed baseline classifier analyzing not just the last, but multiple previous
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a classifier to this degree of precision would require a large-scale experiment to account for the long tail of the
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measurements. Experimentally evaluating a classifier to this degree of precision would require a large-scale experiment
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error distribution.
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to account for the long tail of the error distribution.
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\color{black}
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\color{black}
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Figure~\ref{fig_layout_identity_layout} compares several copies of the same mesh (top left quadrant, 1) to four variants
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Figure~\ref{fig_layout_identity_layout} compares several copies of the same mesh (top left quadrant, 1) to four variants
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