Title: Detecting Hardware Trojans Using Combined Self-Testing and Imaging
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.
Publication Year: 2022
Publication Date: 2022-06-01
Language: en
Type: article
Indexed In: ['crossref']
Access and Citation
Cited By Count: 14
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