DETERRENT
Vasudev Gohil, Satwik Patnaik, Hao Guo, Dileep Kalathil, Jeyavijayan Rajendran
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×) in the number of test patterns required while maintaining or improving coverage (95.75%) compared to the state-of-the-art techniques.
Topics & Concepts
ScalabilityReinforcement learningComputer scienceReduction (mathematics)Set (abstract data type)Parallel computingAlgorithmComputer engineeringArtificial intelligenceMathematicsOperating systemProgramming languageGeometryPhysical Unclonable Functions (PUFs) and Hardware SecurityVLSI and Analog Circuit TestingAdversarial Robustness in Machine Learning