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9
EET1/MLCS_conference/bibliography.bib
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@ -1734,3 +1734,12 @@ pages={328-333},}
organization={IEEE} organization={IEEE}
} }
@article{rohatgi2009electromagnetic,
title={Electromagnetic attacks and countermeasures},
author={Rohatgi, Pankaj},
journal={Cryptographic Engineering},
pages={407--430},
year={2009},
publisher={Springer}
}

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EET1/MLCS_conference/main.tex
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@ -87,10 +87,10 @@ anon@anonymous.nw}
Side-channel emissions provide an independent and extrinsic source of information at the about the system, purely based on the physical by-product of its activities. Side-channel emissions provide an independent and extrinsic source of information at the about the system, purely based on the physical by-product of its activities.
Leveraging side-channel information, we propose a physics-based \gls{ids} as an aditional layer of protection for embedded systems. Leveraging side-channel information, we propose a physics-based \gls{ids} as an aditional layer of protection for embedded systems.
The physic-based \gls{ids} uses machine-learning-based power analysis to monitor and assess the behaviour and integrity of network switches. The physic-based \gls{ids} uses machine-learning-based power analysis to monitor and assess the behaviour and integrity of network equipment.
%The proposed \gls{ids} offers complementary intrusion detection for an HP Procurve Network Switch 5406zl, using its power consumption as side-channel emissions. %The proposed \gls{ids} offers complementary intrusion detection for an HP Procurve Network Switch 5406zl, using its power consumption as side-channel emissions.
The \gls{ids} successfully detect three different classes of attacks on an HP Procurve Network Switch 5406zl: (i)~firmware manipulation with \numprint[\%]{99} accuracy, (ii)~brute-force SSH login attempts with \numprint[\%]{98}, and (iii)~hardware tampering with \numprint[\%]{100}. The \gls{ids} successfully detect three different classes of attacks on an HP Procurve Network Switch 5406zl: (i)~firmware manipulation with \numprint[\%]{99} accuracy, (ii)~brute-force SSH login attempts with \numprint[\%]{98} accuracy, and (iii)~hardware tampering with \numprint[\%]{100} accuracy.
The machine-learning models require a small number of power traces for training and still achieve a high accuracy for attack detection. The machine-learning models require a small number of power traces for training and still achieve a high accuracy for attack detection.
The concepts and techniques discussed in the paper can also extend to offer intrusion detection for embedded systems in general. The concepts and techniques discussed in the paper can also extend to offer intrusion detection for embedded systems in general.
@ -111,30 +111,31 @@ To deter cases of cyberattacks, data centers often use \gls{ids}.
Current \glspl{ids} use different approaches to detect intrusions. Current \glspl{ids} use different approaches to detect intrusions.
\glspl{hids} are implemented directly on the monitored device and leverage information provided by the system to detect intrusions. \glspl{hids} are implemented directly on the monitored device and leverage information provided by the system to detect intrusions.
\glspl{nids} leverage network information to detect intrusions at the network level. \glspl{nids} leverage network information to detect intrusions at the network level.
Although \glspl{hids} and \glspl{nids} offer intrusion detection capabilities, they are still quite ineffective against attacks such as firmware modification~\cite{cisco_trust,thomson_2019}, bypassing secure boot-up~\cite{Cui2013WhenFM, hau_2015}, log tampering~\cite{koch2010security}, or hardware tampering\cn. Although \glspl{hids} and \glspl{nids} offer intrusion detection capabilities, they are still quite ineffective against attacks such as firmware modification~\cite{cisco_trust,thomson_2019}, bypassing secure boot-up~\cite{Cui2013WhenFM, hau_2015}, log tampering~\cite{koch2010security}, or hardware tampering\cite{rohatgi2009electromagnetic}.
The literature shows promising work in improving the state-of-the-art in security by analyzing side-channel emissions from embedded systems. The literature shows promising work in improving the state-of-the-art in security by analyzing side-channel emissions from embedded systems.
Systems generate side-channel emissions, which usually reflect their activity in the form of power consumption \cite{kocher1999differential, brier2004correlation, Moreno2018}, electromagnetic waves \cite{khan2019malware, sehatbakhsh2019remote}, acoustic emissions \cite{genkin2014rsa, liuacoustic}, etc. Systems generate side-channel emissions, which usually reflect their activity in the form of power consumption \cite{kocher1999differential, brier2004correlation, Moreno2018}, electromagnetic waves \cite{khan2019malware, sehatbakhsh2019remote}, acoustic emissions \cite{genkin2014rsa, liuacoustic}, etc.
Side-channel based \glspl{ids} analyze side-channel emissions and can complement state-of-art \glspl{ids}, as shown in this paper. Side-channel based \glspl{ids} analyze side-channel emissions and can complement state-of-art \glspl{ids}, as shown in this paper.
The \gls{ids} uses \gls{dsp} and \gls{ml} to detect anomalies or recognize patterns of previously detected intrusions. The \gls{ids} uses \gls{dsp} and \gls{ml} to detect anomalies or recognize patterns of previously detected intrusions.
Thus, using this IDS would improve the security of the embedded system by detecting attacks that regular \gls{ids} fail to identify. Thus, using this \gls{ids} would improve the security of the embedded system by detecting attacks that regular \glspl{ids} fail to identify.
\subsection{Contributions} \subsection{Contributions}
This paper proposes a side-channel-based \gls{ids} that can complement existing \glspl{ids} and improve security for embedded systems. This paper proposes a side-channel-based \gls{ids} that can complement existing \glspl{ids} and improve security for embedded systems.
The side-channel based \gls{ids} can potentially protect any embedded system as a black box and detect a range of attacks against it. The side-channel based \gls{ids} can potentially protect any embedded system treated a black box and detect a range of attacks against it.
Our \gls{ids} is deployed for an HP Procurve 5406zl network switch as a black box. Our \gls{ids} is deployed on an HP Procurve 5406zl network switch as a black box.
The experiments in the paper illustrate the \gls{ids} capabilities of detecting firmware manipulation and hardware tampering attacks against the switch and defending against log entry forging by offering log verification. The experiments in the paper illustrate the \gls{ids} capabilities of detecting firmware manipulation and hardware tampering attacks against the switch and defending against log entry forging through log verification.
The side-channel based \gls{ids} achieves near-perfect accuracy scores despite using relatively straightforward \gls{dsp} methods and \gls{ml} algorithms. The algorithms analyze \gls{ac} and \gls{dc} power consumption of the network switch to detect these attacks. The experiments use a relatively small dataset that contains roughly \numprint{1000} power traces. The side-channel based \gls{ids} achieves near-perfect accuracy scores despite using simple \gls{dsp} methods and \gls{ml} algorithms. The algorithms analyze \gls{ac} and \gls{dc} power consumption of the network switch to detect these attacks.
%The experiments use a relatively small dataset that contains roughly \numprint{1000} power traces.
\subsection{Paper Organization} \subsection{Paper Organization}
The remainder of the paper is organized as follows: The paper is organized as follows:
Section~\ref{sec:Overview} provides an overview of the motivation for the experiments and threat model. Section~\ref{sec:Overview} provides an overview of the motivation for the experiments and threat model.
Section~\ref{Related Work} talks about other side-channel-based approaches for runtime monitoring and integrity assessment. Section~\ref{Related Work} describe other side-channel-based approaches for runtime monitoring and integrity assessment.
Section~\ref{Firmware} covers experiments related to Firmware Manipulation, Section~\ref{Firmware} covers experiments related to firmware manipulation,
Section~\ref{RunTime} covers Log Verification and Auditing, Section~\ref{RunTime} covers log verification and auditing,
and Section~\ref{Hardware} covers Hardware Tampering. and Section~\ref{Hardware} covers hardware tampering.
The paper concludes in Sections~\ref{Discussion} and ~\ref{Conclusion}. The paper concludes in Sections~\ref{Discussion} and ~\ref{Conclusion}.
\section{Overview} \section{Overview}
@ -142,15 +143,13 @@ The paper concludes in Sections~\ref{Discussion} and ~\ref{Conclusion}.
All embedded systems leak information about their operation through side channel emissions. All embedded systems leak information about their operation through side channel emissions.
Side-channel-based \glspl{ids} use \gls{dsp} methods and \gls{ml} algorithms to model the side-channel data and learn patterns that correlate to the system activity. Side-channel-based \glspl{ids} use \gls{dsp} methods and \gls{ml} algorithms to model the side-channel data and learn patterns that correlate to the system activity.
A major part of designing a reliable side-channel \gls{ids} is identifying appropriate side-channel emissions among temperature, vibration, ultrasound, EM, power consumption, etc.; our experiments focus on the system's power consumption. An important part of designing a reliable side-channel \gls{ids} is identifying appropriate side-channel emissions among temperature, vibration, ultrasound, EM, power consumption, etc.
Our experiments focus on the power consumption.
Power consumption is reasonably easy to non-intrusively and reliably measure. Power consumption is reasonably easy to non-intrusively and reliably measure.
Side-channel-based \gls{ids} can complement \gls{hids} and \gls{nids} in offering runtime monitoring and integrity assessment for embedded systems, as shown in Table~\ref{tab:example}. Side-channel-based \gls{ids} can complement \gls{hids} and \gls{nids} in offering runtime monitoring and integrity assessment for embedded systems, as shown in Table~\ref{tab:example}.
Side-channel-based \glspl{ids} run independently from the system they monitor, which renders them more difficult to circumvent compared to \gls{ids} hosted within the system. Side-channel-based \glspl{ids} run independently from the system they monitor, which makes them more difficult to circumvent compared to \gls{ids} hosted by the system.
Because of the independent nature, a malfunction of the \gls{ids} can not disrupt the regular operation of the system. This independence is also beneficial in case of a malfunction of the \gls{ids}, which can not disrupt the regular operation of the system.
This makes the system monitored by the \gls{ids} immune to any operational failure or security vulnerability that the \gls{ids} might have.
This paper presents a case study for using side-channel based \glspl{ids} to offer runtime monitoring and integrity assessment for network equipment.
\begin{table}[htb] \begin{table}[htb]
@ -185,7 +184,7 @@ This paper presents a case study for using side-channel based \glspl{ids} to off
\subsection{Threat Model} \subsection{Threat Model}
\label{subsec:threat-model} \label{subsec:threat-model}
In the context of this work, we consider active attackers that can tamper with the execution of network devices. In the context of this paper, we consider active attackers that can tamper with the execution of network devices.
These attackers can accomplish their goal by assuming different roles and exploiting several mechanisms, as summarized below: These attackers can accomplish their goal by assuming different roles and exploiting several mechanisms, as summarized below:
\textbf{Remote Code Execution:} \textbf{Remote Code Execution:}
@ -197,7 +196,7 @@ A remote attacker could attempt to log in through password guessing, with the ob
\textbf{Unauthorized Firmware Reprogramming (or Failure to Apply a Scheduled Firmware Upgrade):} \textbf{Unauthorized Firmware Reprogramming (or Failure to Apply a Scheduled Firmware Upgrade):}
Either through physical access to the device or upon successful administrative login, the attacker can reprogram the firmware of the device. Either through physical access to the device or upon successful administrative login, the attacker can reprogram the firmware of the device.
The applied firmware can be an older version to reactivate a specific vulnerability, or it could be a custom firmware that contains some backdoor or rootkit. The applied firmware can be an older version to reactivate a specific vulnerability, or it could be a custom firmware that contains backdoors.
\textbf{Unauthorized Hardware Configuration Changes:} \textbf{Unauthorized Hardware Configuration Changes:}
An attacker with physical access to the device could apply undocumented changes to the configuration of the device to its advantage. An attacker with physical access to the device could apply undocumented changes to the configuration of the device to its advantage.
@ -217,10 +216,10 @@ In our setup, the power consumption of the device is measured in two different w
For both \gls{ac} and \gls{dc}, a power measurment box is placed in series with the main power cable. For both \gls{ac} and \gls{dc}, a power measurment box is placed in series with the main power cable.
The box measures the voltage drop generated by the current flowing through a shunt resistor. The box measures the voltage drop generated by the current flowing through a shunt resistor.
This box samples the voltage at one mega sample per seconds (1MSPS). This box samples the voltage at one mega sample per seconds (1MSPS).
During every operation of the device, the different instructions will have impacts on the overall power consumption \cite{727070} and will be detectable in either \gls{ac} and \gls{dc} power consumption. During every operation of the device, the different instructions influence the overall power consumption \cite{727070} and will be detectable in either \gls{ac} and \gls{dc} power consumption.
\gls{ac} powertraces are less intrusive to capture than \gls{dc} power consumption and offer the most transparent way to retrofit the proposed system for different devices. \gls{ac} powertraces are less intrusive to capture than \gls{dc} power consumption and offer the most transparent way to retrofit the proposed system for different devices.
However, its \gls{snr} is lower compared to the \gls{dc} measurement because the \gls{ac}/\gls{dc} switching converter introduces a buffering of electrical energy, thus hiding some of the fine-grained details. However, its \gls{snr} is lower compared to the \gls{dc} measurement because the \gls{ac}/\gls{dc} switching converter introduces a buffering of electrical energy, thus hiding some of the fine-grained details.
Work by Moreno~et~al.~\cite{Moreno2018} uses the power consumption of embedded systems for non-intrusive online runtime monitoring through reconstruction of the program's execution trace. %Work by Moreno~et~al.~\cite{Moreno2018} uses the power consumption of embedded systems for non-intrusive online runtime monitoring through reconstruction of the program's execution trace.
\section{Related Work} \section{Related Work}
\label{Related Work} \label{Related Work}
@ -255,10 +254,12 @@ The system flags an activity as anomalous when the emanations differ from the no
Sehatbakhsh et al.~\cite{sehatbakhsh2019remote} also use EM emanations and detect malware code injection into a known application without any prior knowledge of the malware signature. Sehatbakhsh et al.~\cite{sehatbakhsh2019remote} also use EM emanations and detect malware code injection into a known application without any prior knowledge of the malware signature.
They use HDBSCAN clustering method to identify anomalous behaviour exhibited by the malicious code. They use HDBSCAN clustering method to identify anomalous behaviour exhibited by the malicious code.
Yilmaz et al.~\cite{yilmaz2019detecting} implement K-Nearest Neighbors clustering methods along with PCA dimensionality reduction method to model EM emanations from a phone with the different operational status of front/rear camera. Yilmaz et al.~\cite{yilmaz2019detecting} implement K-Nearest Neighbors clustering methods along with PCA dimensionality reduction method to model EM emanations from a phone with the different operational status of front/rear camera.
Using the ML methods, they can determine the state of cellphone cameras. \\ Using the ML methods, they can determine the state of cellphone cameras.
\indent
The work that this paper proposes builds on top of the aforementioned works. An HP network switch, treated as a black box, generates side-channel leaks in the form of its power consumption. %The work that this paper proposes builds on top of the aforementioned works.
The experiments treat this power consumption as an output of the system when the inputs are certain attacks/stimuli that triggers the switch. The data train the \gls{ml} models, which, in turn, successfully identify the attacks/stimuli on the switch. %An HP network switch, treated as a black box, generates side-channel leaks in the form of its power consumption.
%The experiments treat this power consumption as an output of the system when the inputs are certain attacks/stimuli that triggers the switch.
%The data train the \gls{ml} models, which, in turn, successfully identify the attacks/stimuli on the switch.
\section{Experiment Family I: Firmware Manipulation} \section{Experiment Family I: Firmware Manipulation}
\label{Firmware} \label{Firmware}
@ -298,7 +299,7 @@ Figure~\ref{fig:firmwares} illustrates the captured data for two different firmw
\caption{PSD of power traces of boot-up sequences for two different firmware versions (two traces for each version)} \caption{PSD of power traces of boot-up sequences for two different firmware versions (two traces for each version)}
\label{fig:firmwares-psd} \label{fig:firmwares-psd}
\end{subfigure} \end{subfigure}
\caption{Impact of different firmware versions on the power consumption at boot time.} \caption{Influence of different firmware versions on the power consumption at boot time.}
\label{fig:firmwares} \label{fig:firmwares}
\end{figure} \end{figure}