ATTRITION: Attacking Static Hardware Trojan Detection Techniques Using Reinforcement Learning
Vasudev Gohil, Hao Guo, Satwik Patnaik, Jeyavijayan Rajendran
Abstract
Stealthy hardware Trojans (HTs) inserted during the fabrication of integrated circuits can bypass the security of critical infrastructures. Although researchers have proposed many techniques to detect HTs, several critical limitations exist, including: (i) a low success rate of HT detection, (ii) high algorithmic complexity, and (iii) a large number of test patterns. Furthermore, as we show in this work the most pertinent drawback of prior (including state-of-the-art) detection techniques stems from an incorrect evaluation methodology, i.e., they assume that an adversary inserts HTs randomly. Such inappropriate adversarial assumptions enable detection techniques to claim high HT detection accuracy, leading to a "false sense of security." To the best of our knowledge, despite more than a decade of research on detecting HTs inserted during fabrication, there have been no concerted efforts to perform a systematic evaluation of HT detection techniques.