HTDet: A clustering method using information entropy for hardware Trojan detection

Hardware Trojans (HTs) have drawn increasing attention in both academia and industry because of their significant potential threat. In this paper, we propose HTDet, a novel HT detection method using information entropy-based clustering. To maintain high concealment, HTs are usually inserted in the regions with low controllability and low observability, which will result in that Trojan logics have extremely low transitions during the simulation. This implies that the regions with the low transitions will provide much more abundant and more important information for HT detection. The HTDet applies information theory technology and a density-based clustering algorithm called Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to detect all suspicious Trojan logics in the circuit under detection. The DBSCAN is an unsupervised learning algorithm, that can improve the applicability of HTDet. In addition, we develop a heuristic test pattern generation method using mutual information to increase the transitions of suspicious Trojan logics. Experiments on circuit benchmarks demonstrate the effectiveness of HTDet.