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@ -144,10 +144,10 @@ In this paper, we focus on assisting the recovery procedure after a succesful at
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approach will yield a better return of investement in overall grid stability versus resources spent on security
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measures. Previous work on IoT and Smart Grid security has focused on the prevention of attacks though firmware security
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measures. While research on prevention is important, we estimate that its practical impact will be limited by the
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diversity of implementations found in the field~\cite{nbck+19,zlmz+21}. We predict that it would be a Sisyphean task to
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secure the firmware of sufficiently many devices to deny an attacker the critical mass needed to cause trouble. Even if
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all flaws in the firmware of a broad range of devices would be fixed, users still have to update. In smart grid and IoT
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devices, this presents a difficult problem since user awareness is low~\cite{nbck+19}.
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diversity of implementations found in the field~\cite{nbck+19,zlmz+21,smp18}. We predict that it would be a Sisyphean
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task to secure the firmware of sufficiently many devices to deny an attacker the critical mass needed to cause trouble.
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Even if all flaws in the firmware of a broad range of devices would be fixed, users still have to update. In smart grid
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and IoT devices, this presents a difficult problem since user awareness is low~\cite{nbck+19}.
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\subsection{Contents}
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@ -175,8 +175,8 @@ deviation $f_\Delta$ that the modulated carrier deviates from its nominal value
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milli-Hertz.
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When grid frequency is measured by first digitizing the mains voltage waveform, then de-modulating digitally, the FM's
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SNR is very high and is dominated by the ADC's quantization noise and nearby mains voltage noise sources such as
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resistive droop due to large inrush current of nearby machines.
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signal-to-noise ratio (SNR) is very high and is dominated by the ADC's quantization noise and nearby mains voltage noise
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sources such as resistive droop due to large inrush current of nearby machines.
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Note that both the carrier signal at $f_c$ and the modulation signal at $f_m$ both have unit Hertz. To disambiguate
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them, in this paper we will use \textbf{bold} letters to refer to the carrier waveform $\mathbf{U}$ or frequency
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@ -357,6 +357,18 @@ line.
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\subsection{Proposed Countermeasures}
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In~\cite{kgma21}, the authors propose an extension to grid control algorithms aimed at increasing the grid's robustness
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towards forced oscillations. In~\cite{smp18}, the authors propose that utility operators use a detailed attacker model
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to engineer additional safety margins into the grid while minimizing the economic inefficiency of these measures. On the
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IoT side, they note that due to the wide implementation diversity, the problem cannot be solved by individual measures
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and propose additional fundamental research on IoT device security.
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In~\cite{hcb19}, the authors conclude that simple demand attacks where compromised loads suddenly increase demand are
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adequately mitigated by existing safety measures, in particular \emph{Under-Frequency Load Shedding} (UFLS). As part of
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UFLS, during a contingency the utility will progressively disconnected loads according to set priorities until the
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production / generation balance has been restored and a blackout has been averted. UFLS is already deployed in any large
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electrical grid.
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% FIXME more sources!
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\section{Grid Frequency as a Communication Channel}
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@ -488,9 +500,9 @@ parts of the plant, as this is commonplace during routine maintenance activities
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Given the grid characteristics we measured using our custom waveform recorder and using a model of our transmitter, we
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can derive parameters for the modulation of our broadcast system. The overall network power-frequency characteristic of
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the continental European synchronous area is about $\SI{25}{\giga\watt\per\hertz}$~\cite{entsoe02}. Thus, the main
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challenge for a GFM system will be poor SNR due to low transmission power. A second layer of modulation yielding some
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modulation gain beyond the basic amplitude modulation of the transmitter will be necessary to achieve sufficient overall
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SNR.
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challenge for a GFM system will be poor signal-to-noise ratio (SNR) due to low transmission power. A second layer of
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modulation yielding some modulation gain beyond the basic amplitude modulation of the transmitter will be necessary to
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achieve sufficient overall SNR.
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The grid's frequency noise has significant localized peaks that might interfere with this modulation. Further
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complicating things are the oscillation modes. A GFM system must be designed to avoid exciting these modes. However,
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@ -505,22 +517,46 @@ overall performance. DSSS chip timing should be as fast as the transmitter's phy
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region between $\SI{0.2}{\hertz}$ to $\SI{2.0}{\hertz}$ in Figure~\ref{fig_freq_spec}. Going past
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$\approx\SI{2}{\hertz}$ would complicate frequency measurement at the receiver side.
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\paragraph{Direct Sequence Spread Spectrum (DSSS) modulation}
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\subsubsection{Direct Sequence Spread Spectrum (DSSS) modulation}
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% FIXME quickly explain DSSS here.
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Direct Sequence Spread Spectrum modulation is a common spread-spectrum technique that forms the basis of a number of
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radio systems, most prominently all global navigation satellite systems (GNSS). As a spread-spectrum technique, DSSS
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spreads out the signal's energy across a broad spectral range. This decreases the susceptibility of a DSSS signal to
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narrowband interference. In GNSS, this allows the rejection of other nearby RF sources. In our use case, this makes the
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signal immune to the many narrow peaks in the grid frequency's noise spectrum that are caused by UTC-synchronized
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control systems (cf.~Fig.~\ref{fig_freq_spec}). In addition to better interference immunity, DSSS has two other
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important characteristics: It provides \emph{modulation gain}, i.e.~it allows a trade-off between data rate and receiver
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sensitivity, and it allows for Code Division Multiple Access (CDMA). In CDMA, multiple DSSS-modulated signals can be
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sent simultaneously through a shared channel with less impact to the resulting signal-to-noise ratio (SNR) than would be
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the case for other modulation techniques.
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\paragraph{DSSS parametrization}
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A DSSS signal is made up from pseudo-random \emph{symbols}, which in turn are made up from individual physical layer
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bits called \emph{chips}. Chips are encoded in the signal using a lower-layer modulation such as phase-shift keying
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(e.g.~in GPS) or frequency-shift keying (in this work). In DSSS, a \emph{code} is a library of symbols that are
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constructed to have minimal cross-correlation, meaning they are near-orthogonal. A transmitter sends a symbol by
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transmitting its particular pseudo-random chip sequence at a chosen polarity, conveying one bit of information. A
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receiver demodulates the signal by directly correlating the incoming physical-layer signal with the symbol's chip
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pattern, which results in a positive or negative peak depending on symbol polarity when a symbol is received.
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We simulated a proof-of-concept modulator and demodulator using data captured from our grid frequency sensor. Our
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simulations covered a range of parameters in modulation amplitude, DSSS sequence bit depth, chip duration and detection
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threshold. Figure~\ref{fig_ser_nbits} shows our simulation results for symbol error rate (SER) as a function of
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modulation amplitude with Gold sequences of several bit depths. From these graphs we conclude that the range of
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practical modulation amplitudes starts at approximately $\SI{1}{\milli\hertz}$, which corresponds to a modulation power
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of approximately $\SI{25}{\mega\watt}$~\cite{entsoe02}. Figure~\ref{fig_ser_thf} shows SER against detection threshold
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relative to background noise. Figure~\ref{fig_ser_chip} shows SER against chip duration for a given fixed symbol length.
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As expected from looking at our measured grid frequency noise spectrum, performance is best for short chip durations and
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worsens for longer chip durations since shorter chip durations move our signals' bandwidth into the lower-noise region
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from $\SI{0.2}{\hertz}$ to $\SI{2}{\hertz}$.
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By increasing the DSSS sequence length by a factor of $2$, SNR is improved by $\sqrt{2}$ assuming an additive white
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gaussian noise (AWGN) channel. At the same time, when doubling the sequence length, common DSSS code construction
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methods provide twice the number of distinctive symbols allowing for twice the number of CDMA participants. The trade
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off between twice the sequence length (and transmission time) for approximately $\SI{1.5}{dB}$ in SNR is a steep
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trade-off, but is necessary in systems where transmitter power cannot be increased further and the resulting signal has
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a marginally low SNR.
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\subsubsection{DSSS parametrization}
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To find the parameters for our DSSS modulation, we simulated a proof-of-concept modulator and demodulator using data
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captured from our grid frequency sensor. Our simulations covered a range of combinations of modulation amplitude, DSSS
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sequence bit depth, chip duration and detection threshold. Figure~\ref{fig_ser_nbits} shows our simulation results for
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symbol error rate (SER) as a function of modulation amplitude with Gold sequences of several bit depths. From these
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graphs we conclude that the range of practical modulation amplitudes starts at approximately $\SI{1}{\milli\hertz}$,
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which corresponds to a modulation power of approximately $\SI{25}{\mega\watt}$~\cite{entsoe02}.
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Figure~\ref{fig_ser_thf} shows SER against detection threshold relative to background noise. Figure~\ref{fig_ser_chip}
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shows SER against chip duration for a given fixed symbol length. As expected from looking at our measured grid frequency
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noise spectrum, performance is best for short chip durations and worsens for longer chip durations since shorter chip
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durations move our signals' bandwidth into the lower-noise region from $\SI{0.2}{\hertz}$ to $\SI{2}{\hertz}$.
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%FIXME introduce term "chip" somewhere
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\begin{figure}
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