Scalable Hardware Trojan Diagnosis

Abstract

Hardware Trojans (HTs) pose a significant threat to the modern and pending integrated circuit (IC). Due to the diversity of HTs and intrinsic process variation (PV) in IC design, detecting and locating HTs is challenging. Several approaches have been proposed to address the problem, but they are either incapable of detecting various types of HTs or unable to handle very large circuits. We have developed a scalable HT detection and diagnosis approach that uses segmentation and gate level characterization (GLC). We ensure the detection of arbitrary malicious circuitry by measuring the overall leakage current for a set of different input vectors. In order to address the scalability issue, we employ a segmentation method that divides the large circuit into small sub-circuits using input vector selection. We develop a segment selection model in terms of properties of segments and their effects on GLC accuracy. The model parameters are calibrated by sampled data from the GLC process. Based on the selected segments we are able to detect and diagnose HTs by tracing gate level leakage power. We evaluate our approach on several ISCAS85/ISCAS89/ITC99 benchmarks. The simulation results show that our approach is capable of detecting and diagnosing HTs accurately on large circuits.