NetVGE: Netwise Hardware Trojan Detection at RTL Using Variable Dependency and Knowledge Graph Embedding
Yaroslav Popryho, Debjit Pal, Inna Partin-Vaisband · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · 2025
Hardware Trojans (HTs) can be maliciously inserted in integrated circuits (ICs) during various phases of the circuit design process, posing significant security risks. Existing solutions are limited by their dependency on golden references, poor scalability, and suffer from high false positive and negative rates, making them less effective in detecting and mitigating HTs. In this paper, a NetVGE framework is proposed for efficient HT detection at the register-transfer (RT) level. NetVGE generates weighted variable dependency graphs, which are embedded in a latent space in an unsupervised manner using the knowledge graph embedding (KGE) algorithm. The embedded HTs are accurately detected in the latent space with a HittER model. The effectiveness of NetVGE is demonstrated based on RT-level Trust-HUB benchmarks, yielding high recall (93%) and precision (98%). These results validate NetVGEs effectiveness for realworld hardware cybersecurity applications and demonstrate its scalability with increasing IC size compared to state-of-the-art HT detection methods.