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 the regions with low controllability observability, which will result that Trojan logics extremely transitions during simulation. This implies provide much more abundant important for detection. The HTDet applies theory technology density-based clustering algorithm called Density-Based Spatial Clustering Applications Noise (DBSCAN) to detect all suspicious circuit under DBSCAN is an unsupervised learning algorithm, can improve applicability HTDet. addition, develop heuristic test pattern generation mutual increase logics. Experiments on benchmarks demonstrate effectiveness