DETERRENT: Detecting Trojans using Reinforcement Learning

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.

arXiv commentPublished in 2022 Design Automation Conference (DAC)

References in corpus (2)

• Deep Reinforcement Learning for Cyber Security
• DETERRENT: Detecting Trojans using Reinforcement Learning

Cited by in corpus (6)

• DETERRENT: Detecting Trojans using Reinforcement Learning
• Trojan Playground: A Reinforcement Learning Framework for Hardware Trojan Insertion and Detection
• Hiding in Plain Sight: Reframing Hardware Trojan Benchmarking as a Hide&Seek Modification
• TrojanForge: Generating Adversarial Hardware Trojan Examples Using Reinforcement Learning
• SCARF: Securing Chips with a Robust Framework against Fabrication-time Hardware Trojans
• Design for Assurance: Employing Functional Verification Tools for Thwarting Hardware Trojan Threat in 3PIPs