HTDet: A Clustering Method using Information Entropy for Hardware Trojan Detection

TL;DR

This paper introduces HTDet, a novel hardware Trojan detection method that uses information entropy-based clustering and heuristic test pattern generation to identify suspicious low-activity regions in circuits.

Contribution

HTDet combines information entropy and density-based clustering to effectively detect hardware Trojans, improving applicability with an unsupervised approach and heuristic test pattern generation.

Findings

Effective detection of Trojans in benchmarks

High accuracy in identifying low-activity Trojan regions

Improved detection applicability with unsupervised clustering

Abstract

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.