Hardware Trojan Detection and High-Precision Localization in NoC-based MPSoC using Machine learning
Hardware Trojan Detection and High-Precision Localization in NoC-based MPSoC using Machine learning
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DOI：
10.1145/3566097.3567922
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发表时间：
2023-01
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影响因子：
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通讯作者：
Haoyu Wang;Basel Halak
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文献类型：
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作者：
Haoyu Wang;Basel Halak
基于片上网络（NoC）的多处理器片上系统（MPSoC）在工业和消费电子领域得到越来越多的应用。在基于片上网络的MPSoC中，外包第三方IP（3 PIP）和工具是大多数无晶圆厂公司普遍采用的开发方式。然而，硬件木马（HT）在其设计阶段注入可以恶意篡改该通信方案的功能，这破坏了系统的安全性，并可能导致失败。高精度地检测和定位HT是当前技术的挑战。这项工作首次提出了一种新的方法，允许检测和高精度定位的HT，这是基于使用的数据包信息和机器学习算法。该算法采用一种新的动态置信区间（DCI）算法检测恶意数据包，采用一种新的动态安全信用表（DSCT）算法定位HT。我们评估了所提出的框架上运行的网格片上网络真实的工作负载。实验结果表明，平均检测精度为96.3%，平均定位精度为100%，最小定位时间在5.8 ~ 12.9us之间（2GHz）。
Networks-on-Chips (NoC) based Multi-Processor System-on-Chip (MPSoC) are increasingly employed in industrial and consumer elec-tronics. Outsourcing third-party IPs (3PIPs) and tools in NoC-based MPSoC is a prevalent development way in most fabless companies. However, Hardware Trojan (HT) injected during its design stage can maliciously tamper with the functionality of this communication scheme, which undermines the security of the system and may cause a failure. Detecting and localizing HT with high pre-cision is a challenge for current techniques. This work proposes for the first time a novel approach that allows detection and high-precision localization of HT, which is based on the use of packet information and machine learning algorithms. It is equipped with a novel Dynamic Confidence Interval (DCI) algorithm to detect ma-licious packets, and a novel Dynamic Security Credit Table (DSCT) algorithm to localize HT. We evaluated the proposed framework on the mesh NoC running real workloads. The average detection precision of 96.3% and the average localization precision of 100% were obtained from the experiment results, and the minimum HT localization time is around 5.8 ~ 12.9us at 2GHz depending on the different HT-infected nodes and workloads.