基于IFCM加权的SVDD硬件木马检测方法

Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-08-13 Cooperative journals: 《计算机应用研究》

魏延海 李雄伟 张阳 胡晓阳 张坤鹏

Abstract： This paper proposed a hardware Trojan (HT) detection method based on IFCM Weighted SVDD (IFCMW_SVDD) aimed at solving the problem that a great variety of Hardware Trojans that is difficult to obtain unknown Trojan Features characteristics, and the collected Side-channel signal contains noise problems. The traditional Support Vector Data Description (SVDD) has the defective of training all the samples of the same conditions when solving the single classification problem, samples need to be divided into primary and secondary parts according to their problems and trained. But this algorithm calculates the membership degree of the “gold chip " bypass signal by Improved Fuzzy C-means Method (IFCM) , and uses it as the Weight (W) coefficient of the sample feature , the support vectors of constructed SVDD model for the Hardware Trojan detection problem can describe the “golden chip” signals while minimizing the description range. Experiments show that the method proposed to this paper achieves the detection of single-class Hardware Trojans and has higher detection accuracy and stability than the traditional SVDD algorithm.

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