基于遗传算法的硬件木马检测方法

A Novel Hardware Trojan Detection Method Based on Genetic Algorithm

针对硬件木马严重威胁到芯片的安全性和系统的可靠性的问题,提出了一种基于遗传算法的集成电路硬件木马检测方法,该方法将K均值算法的局部收敛能力和遗传算法的全局收敛能力结合起来,提取旁路信息间的微小特征差异,实现硬件木马的在线自动检测.本实验在FPGA芯片实现硬件验证,以全局时钟、动态功耗和环形振荡器的输出三个信号作为研究对象,搭建集成电路硬件木马检测系统采集三维旁路信息,对160个样本芯片数据进行聚类分析,实验结果证明该方法可准确有效地检测出硬件木马,检测分辨率达到10-4量级.

Hardware Trojan, a malicious circuit inserted into the golden circuit, causes a serious risk to the security of Integrated Circuits and the trust of critical system. Thus, hardware Trojan detection is of great significance. A novel hardware Trojan detection method based on clustering analysis algorithm is presented in this work, which employs the global optimized ability of genetic algorithm and the local searching capability of K-means algorithm to automatically fulfill the detection online. Side-channel signals, such as global clock signal, reset signal and a ring oscillator signal of a chip, are applied as the inputs of the clustering algorithm, since they may be altered by the Trojan circuit. Experimental results implemented with FPGAs demonstrate that the tested chips can be clustered into two categories, and the Trojan chips can be distinguished from the Golden chips accurately. The detection resolution achieves about 10.4 , which indicates the proposed novel detection method is feasible and effective.