Defeating Untrustworthy Testing Parties: A Novel Hybrid Clustering Ensemble Based Golden Models-Free Hardware Trojan Detection Method

• Main Authors: Mingfu Xue, Rongzhen Bian, Weiqiang Liu, Jian Wang
• Format: Article
• Language: English
• Published: IEEE 2019-01-01
• Series: IEEE Access
• Subjects: Hardware security; hardware Trojan detection; untrustworthy testing parties; unsupervised learning; clustering ensemble
• Online Access: https://ieeexplore.ieee.org/document/8580552/

Due to the globalization of the design and fabrication process of integrated circuits (ICs), ICs are becoming vulnerable to hardware Trojans. Most of the existing hardware Trojan detection works assume that the testing stage is trustworthy. However, testing parties may collude with malicious attackers and modify the results of hardware Trojan detection. In this paper, two attack models for untrustworthy testing parties are formulated. We further propose an adversarial data generation method for untrustworthy testing parties to modify the collected test data. Then, we propose a novel hybrid clustering ensemble method to build a trusted hardware Trojan detection method (clustering ensemble-based hardware Trojan detection method) against untrustworthy testing parties. To alleviate the impact of process variations and noises on hardware Trojan detection in the actual measurement, the unsupervised correlation-based feature selection method is exploited to process the raw test data of ICs for feature selection. The proposed method can eliminate the need of the fabricated golden chips and the simulated golden models. It can also resist the malicious modifications on Trojan detection results introduced by untrustworthy testing parties. Besides, the following problems and questions are also theoretically analyzed and answered: 1) the number of necessary testing parties; 2) the time overhead and the computational overhead of the proposed method; 3) how to choose the basic clustering algorithms (by using a proposed diversity analysis algorithm); and 4) the reason why the proposed clustering ensemble method is superior to the majority voting method. Both the EDA evaluation on ISCAS89 benchmarks and field-programmable gate array evaluation on Trust-HUB benchmarks are performed to evaluate the performance of the proposed method. Experimental results demonstrate that the proposed method can resist malicious modifications robustly and can detect hardware Trojans with high accuracy (up to 93.75%). Meanwhile, the introduced time overhead is small.