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Arthur Grisel-Davy 2023-06-18 18:16:57 -04:00
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EET1/MLCS_conference/main.tex
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@ -91,7 +91,7 @@ Anon. Anonymous
Side-channel emissions provide an independent and extrinsic source of information about the system, purely based on the physical by-product of its activities. Side-channel emissions provide an independent and extrinsic source of information 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 additional layer of protection for embedded systems. Leveraging side-channel information, we propose a physics-based \gls{ids} as an additional 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 equipment. The physics-based \gls{ids} uses machine-learning-based power analysis to monitor and assess the behaviour and integrity of network equipment.
The \gls{ids} successfully detects 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 \gls{ids} successfully detects 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.