Building an accurate hardware Trojan detection technique from inaccurate simulation models and unlabelled ICs

Most of prior hardware Trojan detection approaches require golden chips for references. A classification-based golden chips-free hardware Trojan detection technique has been proposed in the authors' previous work. However, the algorithm in that work is trained by simulated ICs without considering a shift between the simulation and silicon fabrication. In this study, a co-training based hardware Trojan detection method by exploiting inaccurate simulation models and unlabeled fabricated ICs is proposed to provide reliable detection capability when facing fabricated ICs, which eliminates the need of golden chips. Two classification algorithms are trained using simulated ICs. These two algorithms can identify different patterns in the unlabelled ICs during test-time, and thus can label some of these ICs for the further training of the other algorithm. Moreover, a statistical examination is used to choose ICs labelling for the other algorithm. A statistical confidence interval based technique is also used to combine the hypotheses of the two classification algorithms. Furthermore, the partial least squares method is used to preprocess the raw data of ICs for feature selection. Both EDA experiment results and field programmable gate array (FPGA) experiment results show that the proposed technique can detect unknown Trojans with high accuracy and recall.