一种深度学习的硬件木马检测算法

Hardware Trojan detection algorithm based on deep learning

由于传统的硬件木马检测均采用功能测试等电信号检测技术,检测方法存在成本高、漏检率高和效率低下等问题,对此提出了一种深度学习的非电信号硬件木马检测算法。该算法首先利用增强残差网络将低分辨率芯片显微图像转换为高分辨率芯片显微图像;然后通过循环一致对抗生成网络将该高分辨率图像生成与母版图像同源的芯片显微图像,生成的芯片显微图像通过二阶微分图像增强算法区分出目标区域与背景区域,并结合阈值分割算法将目标区域分割出来;最后通过数学形态学操作去除由于工业噪声产生的微小干扰,利用变化检测算法检测芯片中存在的硬件木马。通过在芯片显微图像数据集上的实验显示,基于深度学习的硬件木马检测方法正检率高达约92.4%,与传统的电信号检测方法相比,精度更高,速度更快,且操作更简易。

The traditional way of hardware trojan detection based on electrical signal detection has problems of low positive rate,low efficiency and high cost.To solve these problems,we propose a new way of hardware trojan detection based on deep learning which is not electrical signal detection.First,the algorithm changes chip microscopic images of low resolution into chip microscopic images of high resolution by using an enhanced residual network.Then these chip microscopic images of high resolution will generate another chip microscopic images which are similar to those of the golden model.The algorithm for image enhancement distinguishes between target area and background area by combining with the algorithm of image segmentation.Finally,we use the change detection algorithm to detect the hardware trojans existing in the chip after removing minor interference due to industrial noise.Through the experiments on the micrograph dataset of the chip,the positive detection rate of the hardware trojan detection method based on deep learning is as high as 92.4%.Compared with the traditional electrical signal detection method,our algorithm has the advantages of higher precision,faster speed,and easier operation.