| Title | Hardware Trojan Detection Method for Inspecting Integrated Circuits Based on Machine Learning |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Wang, Yuze, Liu, Peng, Han, Xiaoxia, Jiang, Yingtao |
| Conference Name | 2021 22nd International Symposium on Quality Electronic Design (ISQED) |
| Keywords | composability, detection model, feature extraction, gate-level, Hardware, hardware Trojan detection, Integrated circuit interconnections, Logic gates, machine learning, pubcrawl, resilience, Resiliency, Supply chains, trigger net, trojan horse detection, Trojan horses |
| Abstract | Nowadays malicious vendors can easily insert hardware Trojans into integrated circuit chips as the entire integrated chip supply chain involves numerous design houses and manufacturers on a global scale. It is thereby becoming a necessity to expose any possible hardware Trojans, if they ever exist in a chip. A typical Trojan circuit is made of a trigger and a payload that are interconnected with a trigger net. As trigger net can be viewed as the signature of a hardware Trojan, in this paper, we propose a gate-level hardware Trojan detection method and model that can be applied to screen the entire chip for trigger nets. In specific, we extract the trigger-net features for each net from known netlists and use the machine learning method to train multiple detection models according to the trigger modes. The detection models are used to identify suspicious trigger nets from the netlist of the integrated circuit under detection, and score each net in terms of suspiciousness value. By flagging the top 2% suspicious nets with the highest suspiciousness values, we shall be able to detect majority hardware Trojans, with an average accuracy rate of 96%. |
| DOI | 10.1109/ISQED51717.2021.9424314 |
| Citation Key | wang_hardware_2021 |
Hardware Trojan Detection Method for Inspecting Integrated Circuits Based on Machine Learning