A machine learning based golden-free detection method for command-activated hardware Trojan
A machine learning based golden-free detection method for command-activated hardware Trojan
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DOI：
10.1016/j.ins.2020.05.053
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发表时间：
2020-11
期刊：
影响因子：
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通讯作者：
Ning Shang;An Wang;Yaoling Ding;Keke Gai;Liehuang Zhu;Guoshuang Zhang
中科院分区：
文献类型：
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作者：
Ning Shang;An Wang;Yaoling Ding;Keke Gai;Liehuang Zhu;Guoshuang Zhang
自从第一个硬件木马被发现以来，硬件木马的检测就成为了一个备受关注的研究课题，而激活硬件木马的一种常见方式就是发送命令，检测那些由命令激活的木马是保护硬件安全的一个重要方面。在本文中，我们提出了一种新的无芯片检测方法，称为修剪木马命令搜索（PBCS），这是一种基于机器学习的方法，可以有效地发现命令激活硬件木马。建议PBCS已在实验环境（通过微控制器）和现实世界的验证（智能卡）进行了评估。我们的方法还结合了新奇检测和离群点检测方法，并分别在五个场景中考察了单类支持向量机，局部离群点因子和隔离森林作为分类器的效果。评估结果表明，我们的方法是称职的搜索未知的命令。当选择适当的校准器时，可以提高精度性能。结果表明，PBCS可以成功地找出所有的可执行命令在一个不确定的解析路径的硬件，这意味着我们的方法是适用于复杂的上下文。
Hardware Trojan detection has been becoming an attentive research subject since the first Trojan in real-world hardware was found. A common way to activate a hardware Trojan is to send a command, and detecting those command-activated Trojan is one of the significant dimensions in securing hardware. In this paper, we propose a novel chip-free detection method, calledPruning Bytes Command Search(PBCS), which is a machine learning-based approach and can efficiently find out command-activate hardware Trojans. The proposed PBCS has been evaluated in experimental environments (via micro-controller) and real-world validations (on smart cards). Our approach also combines with novelty detection and outlier detection methods and examines effects onOne-ClassSupport Vector Machine,Local Outlier Factor, and Isolation Forest as distinguishers in five scenes, respectively. The findings of the evaluation show that our approach is competent for searching unknown commands. Accuracy performance can be enhanced when proper distinguishers are selected. The results demonstrate that PBCS can successfully find out all executable commands in an uncertain parsing path hardware, which implies our approach is applicable in the complicated context.