A Feature-Adaptive and Scalable Hardware Trojan Detection Framework For Third-party IPs Utilizing Multilevel Feature Analysis and Random Forest.

Liu, Yanjiang; Li, Junwei; Guo, Pengfei; Zhu, Chunsheng; Wang, Junjie; Zhong, Jingxin; Zhang, Lichao

The trustworthiness of integrated circuits is susceptible to the hardware Trojans in the third-party intellectual property (IP) cores. Various hardware Trojan detection approaches have been proposed to identify the Trojans from a given netlist at the design stage, however, such methods suffer from low detection accuracy, poor scalability, small-scale circuit, and inability to cover more types of Trojans. To address these issues, a hardware Trojan detection framework for third-party IPs is proposed, which is capable of detecting the various types of Trojans with high accuracy and scalable to find the unknown Trojans. Some typical gate-level Trojans in the benchmark circuits are analyzed and 13 common features are introduced as the Trojan features. Furthermore, a feature extraction method is presented to extract 13 features of each node from 23 gate-level circuits and form the Trojan's feature set. Besides, a feature rebalancing method is proposed to balance the distribution of genuine and Trojan class, and a feature combination selection method is proposed to choose the optimal Trojan feature combination of each Trojan circuit, and thus the Trojan's feature database is established. Finally, the Trojan detection is formulated as the anomalous feature identification problem, and a feature-adaptive classifier based on the random forest is proposed to identify various types and features of Trojans. Simulation results of 23 Trojan circuits demonstrate that the proposed approach achieves a comparable precision (TPR=90.48%), recall (TNR=97.92%), and accuracy (ACC=97.96%) compared to the existing methods. Besides, the proposed approach is not only able to detect the existing Trojans in the circuits that scale from 0.2K to 120K nodes, but more importantly, it also has the unique advantage of identifying more types of Trojans by adding new Trojan features to the Trojan feature database.

HARDWARE Trojans (Computers); RANDOM forest algorithms; DATABASES; FEATURE extraction; INTEGRATED circuits

Journal of Electronic Testing, 2024, Vol 40, Issue 6, p741

0923-8174

Academic Journal

10.1007/s10836-024-06150-6