Hybrid multi‐level hardware Trojan detection platform for gate‐level netlists based on XGBoost

Abstract

Abstract Coping with the problem of malicious third‐party vendors implanting Hardware Trojan (HT) in the circuit design stage, this paper proposes a hybrid‐mode gate‐level hardware Trojan detection platform based on the XGBoost algorithm. This detection platform is composed of multi‐level HT localization and circuit structure based HT detection. Each wire of the circuit is regarded as a node in multi‐level HT localization, and static characteristics of nodes are analysed, combining with dynamic detection to locate HT. The network structure features of the circuit are extracted in modular HT structure detection, aiming to identify HT accurately and rapidly. The hybrid‐mode HT detection platform can efficiently meet various detection requirements, such as HT localization or rapid and accurate HT detection. The experiment results on Trust‐Hub benchmark show that the multi‐level localization can achieve 94.0% location accuracy, and the modular HT structure detection accuracy can achieve 100%. The modular HT structure detection is about four times as fast as the multi‐level HT localization on feature extraction. Therefore, multi‐level localization and modular HT structure detection can be respectively or cooperatively applied for specific HT detection issues, which proves that the proposed hybrid‐mode gate‐level HT detection scheme is practical and effective.

Keywords

• gate‐level netlists
• hardware Trojan detection
• hardware Trojan localization
• XGBoost