DETERRENT: Detecting Trojans using Reinforcement Learning

TL;DR

This paper introduces a reinforcement learning-based method to efficiently detect hardware Trojans in integrated circuits by reducing test patterns while maintaining high detection coverage.

Contribution

It presents a novel RL agent that significantly reduces the number of test patterns needed for Trojan detection, outperforming existing methods.

Findings

169x reduction in test patterns required

95.75% detection coverage achieved

Effective and scalable detection approach

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.