论文标题
使用无监督的深度学习量子钻石显微镜磁场图像的硬件特洛伊木马检测
Hardware Trojan Detection Using Unsupervised Deep Learning on Quantum Diamond Microscope Magnetic Field Images
论文作者
论文摘要
本文介绍了一种在集成电路中检测硬件特洛伊特的方法。无监督的深度学习用于对广泛的视野（4x4毫米$^2 $）进行分类，使用量子钻石显微镜（QDM）拍摄的高空间分辨率磁场图像。使用量子控制技术和改进的钻石材料增强了QDM磁成像，以使磁场敏感性提高4倍，而测量速度比以前的演示速度提高了16倍。这些升级促进了用于硬件特洛伊磁场检测的QDM磁场测量的首次演示。无监督的卷积神经网络和聚类用于从没有人类偏见的600x600像素磁场图像的未标记数据集中推断特洛伊木马的存在。该分析比主成分分析更为准确，该分析是区分使用Trojan Free和Trojan插入逻辑的现场可编程栅极阵列。该框架已在我们使用QDM开发和测量的一组可扩展的木马上进行了测试。可伸缩和trusthub木马可检测到最小特洛伊木马触发大小为总逻辑的0.5％。特洛伊木马检测框架可用于无芯片的无芯片检测，因为芯片身份的知识仅用于评估检测准确性
This paper presents a method for hardware trojan detection in integrated circuits. Unsupervised deep learning is used to classify wide field-of-view (4x4 mm$^2$), high spatial resolution magnetic field images taken using a Quantum Diamond Microscope (QDM). QDM magnetic imaging is enhanced using quantum control techniques and improved diamond material to increase magnetic field sensitivity by a factor of 4 and measurement speed by a factor of 16 over previous demonstrations. These upgrades facilitate the first demonstration of QDM magnetic field measurement for hardware trojan detection. Unsupervised convolutional neural networks and clustering are used to infer trojan presence from unlabeled data sets of 600x600 pixel magnetic field images without human bias. This analysis is shown to be more accurate than principal component analysis for distinguishing between field programmable gate arrays configured with trojan free and trojan inserted logic. This framework is tested on a set of scalable trojans that we developed and measured with the QDM. Scalable and TrustHub trojans are detectable down to a minimum trojan trigger size of 0.5% of the total logic. The trojan detection framework can be used for golden-chip free detection, since knowledge of the chips' identities is only used to evaluate detection accuracy
