Hardware Trojans Detection Through RTL Features Extraction and Machine Learning
Jizhong Yang, Ying Zhang, Yifeng Hua, Jiaqi Yao, Zhiming Mao, Xin Chen · 2021
To ensure the hardware security of integrated circuits, especially considering third-party IP cores (3PIP cores) application in SoC design, a novel Hardware Trojan (HT) detection scheme aimed to Register Transfer Level (RTL) Description is proposed in this paper. By analyzing the structural and signal characteristics of RTL design for suspicious circuits, a mathematical model of RTL nodes is constructed to achieve numeric features relevant to HT and available for Machine Learning (ML). Then a ML classifier for a certain category of circuits is trained by Random Forest algorithm and numerical features are extracted. 22 circuits are applied to training and 22 circuits to detecting in experiments, the results show that the average HT detection rate of our proposed detection method can reach 99.93%.