A machine learning based golden-free detection method for command-activated hardware Trojan

• Published in: Information sciences Vol. 540; pp. 292 - 307
• Main Authors: Shang, Ning, Wang, An, Ding, Yaoling, Gai, Keke, Zhu, Liehuang, Zhang, Guoshuang
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01-11-2020
• Subjects: Chip-free; Hardware Trojan detection; Novelty detection; Outlier detection; PBCS; Power analysis; Power analysis; Outlier detection; Hardware Trojan detection; Novelty detection; Chip-free; PBCS
• ISSN: 0020-0255; 1872-6291
• DOI: 10.1016/j.ins.2020.05.053

Hardware Trojan detection has been becoming an attentive research subject since the first Trojan in real-world hardware was found. A common way to activate a hardware Trojan is to send a command, and detecting those command-activated Trojan is one of the significant dimensions in securing hardware. In this paper, we propose a novel chip-free detection method, called Pruning Bytes Command Search (PBCS), which is a machine learning-based approach and can efficiently find out command-activate hardware Trojans. The proposed PBCS has been evaluated in experimental environments (via micro-controller) and real-world validations (on smart cards). Our approach also combines with novelty detection and outlier detection methods and examines effects on One-Class Support Vector Machine, Local Outlier Factor, and Isolation Forest as distinguishers in five scenes, respectively. The findings of the evaluation show that our approach is competent for searching unknown commands. Accuracy performance can be enhanced when proper distinguishers are selected. The results demonstrate that PBCS can successfully find out all executable commands in an uncertain parsing path hardware, which implies our approach is applicable in the complicated context.