Netwise Detection of Hardware Trojans Using Scalable Convolution of Graph Embedding Clouds

Abstract

Hardware Trojans (HTs) are malicious circuits that can be inserted into integrated circuits (ICs) during the design, manufacturing, or packaging phases. HTs can cause a variety of security and safety problems, such as data theft, denialof-service attacks, and physical damage. A scalable referencefree framework is introduced in this paper for netwise gatelevel detection of existing and unknown HTs. The proposed framework consists of embedding-based automated netlist graph analysis and a supervised convolution-based classification of the individual IC net embeddings. A novel convolution algorithm for learning embedded IC graphs has been developed to overcome fundamental scalability limitation of existing graph convolutional neural networks. The performance of the proposed framework is experimentally demonstrated based on the TrustHub TRIT-TC benchmark suite, yielding a high recall of 96% and a precision of 86% for the netwise detection. The individual HT-compromised nets are highlighted within less than 0.1 seconds in >17, 000gate ICs. The proposed framework provides a scalable way to detect existing and unknown HTs without relying on HT-free reference ICs and can be effectively applied to large complex modern ICs. The unique combination of these characteristics makes the proposed framework more practical for real-world hardware cybersecurity applications as compared with prior art.