摘要
在功耗旁路信号统计模型的基础上,提出了一种基于核最大间距准则的硬件木马检测方法及改进的检测方法.将原始功耗旁路信号映射到高维空间,使其具有更高的可分性,然后再投影到低维子空间,从而发现原始数据中的非线性差异特征,实现功耗旁路信号的非线性特征提取与识别.针对AES加密电路中木马电路的检测实验表明,该方法测得超出检测边界的样本数(792)多于Karhunen-Loève变换(400),取得更好的检测效果.
A hardware Trojan detection method based on kernel maximum margin criterion and an improved detection method are proposed on basis of the statistical model of power side-channel signal. The methods can map the rawpower side-channel signal into a higher dimensional space,where it had a higher separability,and then it is projected onto a low-dimensional subspace,so that non-linear characteristics of differences in the rawdata are found,and nonlinear characteristics extraction and recognition of power side-channel signal are achieved. The detection experiment against the Trojan circuit in AES encryption circuit shows that,the number of samples beyond the detection boundary by the method（ 792） is more than Karhunen-Loève Transform（ 400）,which gets a better detection result.
出处
《电子学报》
EI
CAS
CSCD
北大核心
2017年第3期656-661,共6页
Acta Electronica Sinica
基金
国家自然科学基金(No.61271152
No.51377170)
关键词
集成电路
硬件木马
旁路分析
核函数
最大间距准则
integrated circuit
hardware Trojan
side channel analysis
kernel method
maximum margin criterion
