Description
The success of horizontal side-channel attacks heavily depends on the quality of the traces as well as the correct extraction of interest areas, which are expected to contain relevant leakages. If former is insufficient, this will consequently degrade the identification capability of potential leakage candidates and often render attacks inapplicable. This work assess the relevance of neural networks in the unsupervised context of horizontal attacks to mitigate noise artefacts from the input signal by proposing two methods with alternative training objectives. Their application results in enhanced traces quality and better exploitability using clustering-based horizontal attacks.
Infos pratiques
Prochains exposés
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ML-Based Hardware Trojan Detection in AI Accelerators via Power Side-Channel Analysis
Orateur : Yehya NASSER - IMT Atlantique
Our work discusses the security risks associated with outsourcing AI accelerator design due to the threat of hardware Trojans (HTs), a problem traditional testing methods fail to address. We introduce a novel solution based on Power Side-Channel Analysis (PSCA), where we collect and preprocess power traces by segmenting them and extracting features from both time and frequency domains. This[…]-
SemSecuElec
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Side-channel
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Machine learning
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Hardware trojan
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