  Hardware Trojans (HTs) have drawn more and more attention in both academia
and industry because of its significant potential threat. In this paper, we
proposed a novel HT detection method using information entropy based
clustering, named HTDet. The key insight of HTDet is that the Trojan usually be
inserted in the regions with low controllability and low observability in order
to maintain high concealment, which will result in that Trojan logics appear
extremely low transitions during the simulation. This means that the logical
regions with the low transitions will provide us with much more abundant and
more important information for HT detection. Therefore, HTDet applies
information theory technology and a typical density-based clustering algorithm
called Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to
detect all suspicious Trojan logics in circuit under detection (CUD). DBSCAN is
an unsupervised learning algorithm, which can improve the applicability of
HTDet. Besides, we develop a heuristic test patterns generation method using
mutual information to increase the transitions of suspicious Trojan logics.
Experimental evaluation with benchmarks demenstrates the effectiveness of
HTDet.
