Visible to the public Ensemble-Learning-Based Hardware Trojans Detection Method by Detecting the Trigger Nets

TitleEnsemble-Learning-Based Hardware Trojans Detection Method by Detecting the Trigger Nets
Publication TypeConference Paper
Year of Publication2019
AuthorsWang, Yuze, Han, Tao, Han, Xiaoxia, Liu, Peng
Conference Name2019 IEEE International Symposium on Circuits and Systems (ISCAS)
Date Publishedmay
ISBN Number978-1-7281-0397-6
Keywordscyber physical systems, ensemble learning method, feature extraction, Hardware, hardware-Trojan detection method, IC design phase, IC systems, integrated circuit design, Integrated circuit modeling, learning (artificial intelligence), pubcrawl, resilience, Resiliency, security, supply chain security, suspicious Trigger nets, Training, Training data, Trigger-net features, Trojan circuits, trojan horse detection, Trojan horses, Trojan types

With the globalization of integrated circuit (IC) design and manufacturing, malicious third-party vendors can easily insert hardware Trojans into their intellect property (IP) cores during IC design phase, threatening the security of IC systems. It is strongly required to develop hardware-Trojan detection methods especially for the IC design phase. As the particularity of Trigger nets in Trojan circuits, in this paper, we propose an ensemble-learning-based hardware-Trojan detection method by detecting the Trigger nets at the gate level. We extract the Trigger-net features for each net from known netlists and use the ensemble learning method to train two detection models according to the Trojan types. The detection models are used to identify suspicious Trigger nets in an unknown detected netlist and give results of suspiciousness values for each detected net. By flagging the top n% suspicious nets of each detection model as the suspicious Trigger nets based on the suspiciousness values, the proposed method can achieve, on average, 88% true positive rate, 90% true negative rate, and 90% Accuracy.

Citation Keywang_ensemble-learning-based_2019