计算机工程与应用2019，Vol.55Issue(9)：43-48,6.DOI:10.3778/j.issn.1002-8331.1807-0007
一种面向硬件木马检测的SVDD增量学习改进算法
Improved Incremental SVDD Learning Algorithm for Hardware Trojan Detection
摘要
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.关键词
硬件木马/旁路分析/支持向量数据描述/增量学习Key words
hardware Trojan/ side-channel analysis/ Support Vector Data Description(SVDD)/ incremental learning分类
信息技术与安全科学引用本文复制引用
李雄伟,魏延海,王晓晗,徐璐,孙萍..一种面向硬件木马检测的SVDD增量学习改进算法[J].计算机工程与应用,2019,55(9):43-48,6.基金项目
国家自然科学基金(No.61271152,No.51377170) （No.61271152,No.51377170）
国家青年科学基金(No.61602505) （No.61602505）
河北省自然科学基金(No.F2012506008). （No.F2012506008）
