Biblio

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.

To accurately detect Hardware Trojans in integrated circuits design process, a machine-learning-based detection method at the register transfer level (RTL) is proposed. In this method, circuit features are extracted from the RTL source codes and a training database is built using circuits in a Hardware Trojans library. The training database is used to train an efficient detection model based on the gradient boosting algorithm. In order to expand the Hardware Trojans library for detecting new types of Hardware Trojans and update the detection model in time, a server-client mechanism is used. The proposed method can achieve 100% true positive rate and 89% true negative rate, on average, based on the benchmark from Trust-Hub.