关键词: 硬件木马, 旁路分析, 支持向量数据描述, 增量学习

Abstract: Hardware Trojan detection method based on power side-channel analysis and Support Vector Data Description（SVDD） algorithm, it is necessary to incrementally learn new signal samples to optimize the detection model. For the underfitting problem caused by the unconstrained learning range of the new sample of Incremental SVDD learning（ISVDD）, an SVDD incremental learning algorithm for hardware Trojan detection is proposed. The algorithm uses the variance, mean and median relationship between the new sample and the original sample to construct the adaptive parameter, selects more effective new model training samples to improve model detection accuracy while reducing learning time. A multi-chip FPGA side-channel signals acquisition platform is used to collect the signals of three chips with different process variations, and the same-sized hardware Trojans implemented in each chip are detected. Experimental results show that the proposed algorithm has higher detection accuracy than ISVDD, which verifies its effectiveness.

Key words: hardware Trojan, side-channel analysis, Support Vector Data Description（SVDD）, incremental learning