Visible to the public On application of one-class SVM to reverse engineering-based hardware Trojan detection

TitleOn application of one-class SVM to reverse engineering-based hardware Trojan detection
Publication TypeConference Paper
Year of Publication2014
AuthorsChongxi Bao, Forte, D., Srivastava, A.
Conference NameQuality Electronic Design (ISQED), 2014 15th International Symposium on
Date PublishedMarch
Keywordselectronic engineering computing, fabrication outsourcing, feature extraction, golden model, integrated circuit design, Integrated circuit modeling, integrated circuits, invasive software, Layout, learning (artificial intelligence), one-class support vector machine, one-class SVM, reverse engineering, reverse engineering-based hardware Trojan detection, Support vector machines, test-time detection approach, Training, Trojan horses, Trojan-free IC identification, well-studied machine learning method

Due to design and fabrication outsourcing to foundries, the problem of malicious modifications to integrated circuits known as hardware Trojans has attracted attention in academia as well as industry. To reduce the risks associated with Trojans, researchers have proposed different approaches to detect them. Among these approaches, test-time detection approaches have drawn the greatest attention and most approaches assume the existence of a "golden model". Prior works suggest using reverse-engineering to identify such Trojan-free ICs for the golden model but they did not state how to do this efficiently. In this paper, we propose an innovative and robust reverseengineering approach to identify the Trojan-free ICs. We adapt a well-studied machine learning method, one-class support vector machine, to solve our problem. Simulation results using state-of-the-art tools on several publicly available circuits show that our approach can detect hardware Trojans with high accuracy rate across different modeling and algorithm parameters.

Citation Key6783305