Integrated circuits (IC) are vulnerable to hardware Trojans (HTs) due to the globalization of semiconductor design and outsourcing fabrication. Stealthy HTs which activate malicious aging operations are ususlly hide in normal behaviors.Therefore, it is a challenge to detect those HTs by general test and verification approaches. In this paper, we build an efficient machine learning (ML) framework to classify the genuine and Trojan-insert chips using instruction-level side-channel power characters. Different instructions and HTs are used as feature sets to construct the algorithm models. In order to evaluate the performance of the method, we implemented five HTs benchmarks of MC8051 micro-controller in Altera Stratix II FPGA, and presented analysis on five formulated ML models in both supervised and unsupervised modes. The test results showed that the detection accuracy of supervised Naïve Bayes is 95% in average, which is the highest among the ML models. The supervised SVM consumed the shortest running time, with an average of 0.04 s. We also verified that one-class SVM can be a valuable method without golden reference, which has accuracy in the range from 17% to 72% even in Harsh learning condition.
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