计算机应用研究2017，Vol.34Issue(10)：3073-3076,4.DOI:10.3969/j.issn.1001-3695.2017.10.043
基于朴素贝叶斯分类器的硬件木马检测方法
Hardware Trojan detection based on naive Bayesian classifier
摘要
Abstract
This paper proposed a naive Bayesian classifier to detect the hardware Trojan based on the side-channel analysis,and used the power consumption characteristic to train the classifier.After the completion of the classifier construction,it was able to identify the integrated circuits precisely and classify the circuits under test into different categories.So it could be used to detect the hardware Trojan.Experimental results show that the naive classifier can find out a hardware Trojan which occupies only 1.49％ of the AES module with 2.17％ misjudgment rate.Besides,results of the comparison with the Euclidean distance method show ahigher accuracy rate of discrimination,and the advantage of the naive Bayesian classifier which is able to identify the genuine circuits and the Trojan circuits from the hybrid circuits,is not available from the Mabalanobis distance discrimination method.关键词
侧信道分析/硬件木马/朴素贝叶斯分类器/性能比对Key words
side-channel analysis/hardware Trojan(HT)/naive Bayesian classifier/performance comparison分类
信息技术与安全科学引用本文复制引用
王建新,王柏人,曲鸣,张磊..基于朴素贝叶斯分类器的硬件木马检测方法[J].计算机应用研究,2017,34(10):3073-3076,4.基金项目
中央高校基本科研业务费专项资金资助项目(2014GCYY04) （2014GCYY04）
