Test Generation using Reinforcement Learning for Delay-based Side-Channel Analysis

Reliability and trustworthiness are dominant factors in designing System-on-Chips (SoCs) for a variety of applications. Malicious implants, such as hardware Trojans, can lead to undesired information leakage or system malfunction. To ensure trustworthy computing, it is critical to develop efficient Trojan detection techniques. While existing delay-based side-channel analysis is promising, it is not effective due to two fundamental limitations: (i) The difference in path delay between the golden design and Trojan inserted design is negligible compared with environmental noise and process variations. (ii) Existing approaches rely on manually crafted rules for test generation, and require a large number of simulations, making it impractical for industrial designs. In this paper, we propose a novel test generation method using reinforcement learning for delay-based Trojan detection. This paper makes three important contributions. 1) Unlike existing methods that rely on the delay difference of a few gates, our proposed approach utilizes critical path analysis to generate test vectors that can maximize the side-channel sensitivity. 2) To the best of our knowledge, our approach is the first attempt in applying reinforcement learning for efficient test generation to detect Trojans using delay-based analysis. 3) Our experimental results demonstrate that our method can significantly improve both side-channel sensitivity (59% on average) and test generation time (17x on average) compared to state-of-the-art test generation techniques.