Visible to the public Trojan-Feature Extraction at Gate-Level Netlists and Its Application to Hardware-Trojan Detection Using Random Forest Classifier

TitleTrojan-Feature Extraction at Gate-Level Netlists and Its Application to Hardware-Trojan Detection Using Random Forest Classifier
Publication TypeConference Paper
Year of Publication2017
AuthorsHasegawa, K., Yanagisawa, M., Togawa, N.
Conference Name2017 IEEE International Symposium on Circuits and Systems (ISCAS)
KeywordsBenchmark testing, composability, cyber physical systems, F-measure, feature extraction, gate-level netlist, gate-level netlists, Hardware, hardware trojan, hardware-Trojan detection, hardware-Trojan infected nets, IC design process, integrated circuit design, integrated circuits, invasive software, learning (artificial intelligence), Logic gates, machine learning, machine-learning based hardware Trojan classifier, malicious third-party vendors, Multiplexing, normal nets, pubcrawl, Random Forest, random forest classifier, resilience, Resiliency, trojan horse detection, Trojan horses, Trojan nets, Trojan-feature extraction, TrustHUB benchmarks

Recently, due to the increase of outsourcing in IC design, it has been reported that malicious third-party vendors often insert hardware Trojans into their ICs. How to detect them is a strong concern in IC design process. The features of hardware-Trojan infected nets (or Trojan nets) in ICs often differ from those of normal nets. To classify all the nets in netlists designed by third-party vendors into Trojan ones and normal ones, we have to extract effective Trojan features from Trojan nets. In this paper, we first propose 51 Trojan features which describe Trojan nets from netlists. Based on the importance values obtained from the random forest classifier, we extract the best set of 11 Trojan features out of the 51 features which can effectively detect Trojan nets, maximizing the F-measures. By using the 11 Trojan features extracted, the machine-learning based hardware Trojan classifier has achieved at most 100% true positive rate as well as 100% true negative rate in several TrustHUB benchmarks and obtained the average F-measure of 74.6%, which realizes the best values among existing machine-learning-based hardware-Trojan detection methods.

Citation Keyhasegawa_trojan-feature_2017