The Improved COTD Technique for Hardware Trojan Detection in Gate-level Netlist

Abstract

Hardware Trojans (HTs) have introduced serious security concerns into the integrated circuit design flow as they can undermine circuit operations by leaking sensitive information, causing malfunction, or similar attacks. An earlier-introduced HT detection technique in gate-level netlist, the Controllability and Observability for hardware Trojan Detection (COTD) technique detects HTs based on controllability and observability signals in a circuit and presents a static analysis based on an unsupervised machine learning model to identify HT signals in the circuit. While COTD detects the existence of HTs in a circuit, some work has highlighted the shortcoming of COTD in detecting some HT signals that present similar features as genuine signals. To address this shortcoming, this paper presents an improved COTD technique. The improved COTD technique introduces an iterative unsupervised machine-learning technique to isolate HT signals. Furthermore, the improved COTD is equipped with the Gradual-N-Justification (GNJ) technique to reduce false-positive rates in detecting HT signals. The improved COTD technique is applied to several different combinations of full-scan and partial scan circuits tampered with hard-to-detect sequential HTs. To realize valid and hard-to-detect HTs, a configurable HT insertion platform is utilized. The comprehensive results have shown that the improved COTD is highly scalable. Furthermore, the improved COTD technique does not miss a HT circuit if exists and it offers a false-positive rate as low as 3.4%, on average.