Game Theoretic Hardware Trojan Testing Under Cost Considerations

Brahma, Swastik; Njilla, Laurent; Nan, Satyaki

doi:10.1007/978-3-030-90370-1_14

Cited by 5 publications

(1 citation statement)

References 15 publications

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“…This enables the attacker to "play dumb" and deceive the defender in the learning phase. Brahma et al [29] study HT testing by setting a budget constraint on the testing process while dealing with single and multiple HT types. Later, Nan et al [30] improved this work by adding human errors and biases in the insertion and detection processes.…”

Section: Game Theory For Ht Detectionmentioning

confidence: 99%

Hardware Trojan Insertion Using Reinforcement Learning

Sarihi

Patooghy

Jamieson

et al. 2022

Proceedings of the Great Lakes Symposium on VLSI 2022

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Hardware Trojans (HTs) are undesired design or manufacturing modifications that can severely alter the security and functionality of digital integrated circuits. HTs can be inserted according to various design criteria, e.g., nets switching activity, observability, controllability, etc. However, to our knowledge, most HT detection methods are only based on a single criterion, i.e., nets switching activity. This paper proposes a multi-criteria reinforcement learning (RL) HT detection tool that features a tunable reward function for different HT detection scenarios. The tool allows for exploring existing detection strategies and can adapt new detection scenarios with minimal effort. We also propose a generic methodology for comparing HT detection methods fairly. Our preliminary results show an average of 84.2% successful HT detection in ISCAS-85 benchmarks.

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Section: Game Theory For Ht Detectionmentioning

confidence: 99%