Description
The threat of Hardware Trojan-based Covert Channels (HT-CCs) presents a significant challenge to the security of wireless communications. In this work, we generate in hardware and make open-source a dataset for various HT-CC scenarios. The dataset represents transmissions from a HT-infected RF transceiver hiding a CC that leaks information. It encompasses a wide range of signal impairments, noise levels, and HT insertions, facilitating a robust evaluation of HT-CC attack models and defenses. We also propose a deep learning-based HT-CC detection defense that achieves excellent accuracy on the dataset. It is an one fit all solution that circumvents the cost of integrating several distinct defenses to deal with all known HT-CC scenarios.
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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