Hardware Trojan Detection Method Against Balanced Controllability Trigger Design

Abstract

HT has become a serious threat to the Internet of Things due to the globalization of the integrated circuit industry. To evade functional verification, HTs tend to have at least one trigger signal at the gate-level netlist with a very low transition probability. Based on this nature, previous studies use imbalanced controllability as a feature to detect HTs, assuming that signals with imbalanced controllability are always accompanied by low transition probability. However, this study has found out a way to create a new type of HT that has low transition probability but balanced controllability, against previous methods. Hence, current imbalanced controllability detectors are inadequate in this scenario. To address this limitation, we propose a probability-based detection method that uses unsupervised anomaly analysis to detect HTs. Our proposed method detects not only the proposed HT but also the 580 Trojan benchmarks on Trusthub. Experimental results show that our proposed detector outperforms other detectors, achieving an overall 100% true positive rate and 0.37% false positive rate on the 580 benchmarks.