Biblio

As a result of the globalization of integrated circuits (ICs) design and fabrication process, ICs are becoming vulnerable to hardware Trojans. Most of the existing hardware Trojan detection works suppose that the testing stage is trustworthy. However, testing parties may conspire with malicious attackers to modify the results of hardware Trojan detection. In this paper, we propose a trusted and robust hardware Trojan detection framework against untrustworthy testing parties exploiting a novel clustering ensemble method. The proposed technique can expose the malicious modifications on Trojan detection results introduced by untrustworthy testing parties. Compared with the state-of-the-art detection methods, the proposed technique does not require fabricated golden chips or simulated golden models. The experiment results on ISCAS89 benchmark circuits show that the proposed technique can resist modifications robustly and detect hardware Trojans with decent accuracy (up to 91%).

Integrated circuits (ICs) are becoming vulnerable to hardware Trojans. Most of existing works require golden chips to provide references for hardware Trojan detection. However, a golden chip is extremely difficult to obtain. In previous work, we have proposed a classification-based golden chips-free hardware Trojan detection technique. However, the algorithm in the previous work are trained by simulated ICs without considering that there may be a shift which occurs between the simulation and the silicon fabrication. It is necessary to learn from actual silicon fabrication in order to obtain an accurate and effective classification model. We propose a co-training based hardware Trojan detection technique exploiting unlabeled fabricated ICs and inaccurate simulation models, to provide reliable detection capability when facing fabricated ICs, while eliminating the need of fabricated golden chips. First, we train two classification algorithms using simulated ICs. During test-time, the two algorithms can identify different patterns in the unlabeled ICs, and thus be able to label some of these ICs for the further training of the another algorithm. Moreover, we use a statistical examination to choose ICs labeling for the another algorithm in order to help prevent a degradation in performance due to the increased noise in the labeled ICs. We also use a statistical technique for combining the hypotheses from the two classification algorithms to obtain the final decision. The theoretical basis of why the co-training method can work is also described. Experiment results on benchmark circuits show that the proposed technique can detect unknown Trojans with high accuracy (92% 97%) and recall (88% 95%).