From 5cc9ea1e76a15e881b8e6f7c4f0859fb1714c585 Mon Sep 17 00:00:00 2001 From: Arthur Grisel-Davy Date: Sun, 18 Jun 2023 18:16:57 -0400 Subject: [PATCH] physics --- EET1/MLCS_conference/main.tex | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/EET1/MLCS_conference/main.tex b/EET1/MLCS_conference/main.tex index 969ba35..370441f 100644 --- a/EET1/MLCS_conference/main.tex +++ b/EET1/MLCS_conference/main.tex @@ -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. 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 machine-learning models require a small number of power traces for training and still achieve a high accuracy for attack detection.