DETERRENT: detecting trojans using reinforcement learning
DETERRENT: detecting trojans using reinforcement learning
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威慑：使用强化学习检测木马
DOI：
10.1145/3489517.3530518
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Rajendran, Jeyavijayan
中科院分区：
文献类型：
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作者：
Gohil, Vasudev;Patnaik, Satwik;Guo, Hao;Kalathil, Dileep;Rajendran, Jeyavijayan
在集成电路中插入硬件木马（ht）是一种有害的威胁。由于高温超导在罕见的触发条件下被激活，使用随机逻辑模拟检测它们是不可行的。在这项工作中，我们设计了一个强化学习（RL）代理，它绕过指数搜索空间，并返回最可能检测到ht的最小模式集。在各种基准测试上的实验结果证明了我们的RL代理的有效性和可扩展性，与最先进的技术相比，在保持或提高覆盖率（95.75%）的同时，所需的测试模式数量显著减少（169倍）。
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×) in the number of test patterns required while maintaining or improving coverage (95.75%) compared to the state-of-the-art techniques.