Detecting Hardware Trojans Using Combined Self-Testing and Imaging
Nidish Vashistha, Hangwei Lu, Qihang Shi, Damon L. Woodard, Navid Asadizanjani, Mark Tehranipoor
Abstract
Hardware Trojans are malicious modifications in integrated circuits (ICs) with an intent to breach security and compromise the reliability of an electronic system. This article proposes a framework using self-testing, advanced imaging, and image processing with machine learning to detect hardware Trojans inserted by untrusted foundries. It includes on-chip test structures with negligible power, delay, and silicon area overheads. The core step of the framework is on-chip golden circuit design, which can provide authentic samples for image-based Trojan detection through self-testing. This core step enables a golden-chip-free Trojan detection that does not rely on an existing image data set from Trojan-free chip or image synthesizing. We have conducted an in-depth analysis of detection steps and discussed possible attacks with countermeasures to strengthen this framework. The performance evaluation on a 28-nm FPGA and a 90-nm IC validates its high accuracy and reliability for practical applications.