Hardware Trojan Detection and High-Precision Localization in NoC-based MPSoC using Machine learning

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.