DETERRENT: Detecting Trojans using Reinforcement Learning

• URL: http://arxiv.org/abs/2208.12878v1
• Date: Fri, 26 Aug 2022 22:09:47 GMT
• Title: DETERRENT: Detecting Trojans using Reinforcement Learning
• Authors: Vasudev Gohil, Satwik Patnaik, Hao Guo, Dileep Kalathil, Jeyavijayan (JV) Rajendran
• Abstract summary: Hardware Trojans (HTs) are a pernicious threat to integrated circuits. In this work, we design a reinforcement learning (RL) agent that circumvents the exponential search space and returns a minimal set of patterns that is most likely to detect HTs.
• Score: 8.9149615294509
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Insertion of hardware Trojans (HTs) in integrated circuits is a pernicious threat. Since HTs are activated under rare trigger conditions, detecting them using random logic simulations is infeasible. In this work, we design a reinforcement learning (RL) agent that circumvents the exponential search space and returns a minimal set of patterns that is most likely to detect HTs. Experimental results on a variety of benchmarks demonstrate the efficacy and scalability of our RL agent, which obtains a significant reduction ($169\times$) in the number of test patterns required while maintaining or improving coverage ($95.75\%$) compared to the state-of-the-art techniques.

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