NtNDet: Hardware Trojan detection based on pre-trained language models

Hardware Trojans (HTs) are malicious modifications embedded in Integrated Circuits (ICs) that pose a significant threat to security. The concealment of HTs and the complexity of IC manufacturing make them difficult to detect. An effective solution is identifying HTs at the gate level through machine learning techniques. However, current methods primarily depend on end-to-end training, which fails to fully utilize the advantages of large-scale pre-trained models and transfer learning. Additionally, they do not take advantage of the extensive background knowledge available in massive datasets. This study proposes an HT detection approach based on large-scale pre-trained NLP models. We propose a novel approach named NtNDet, which includes a method called Netlist-to-Natural-Language (NtN) for converting gate-level netlists into a natural language format suitable for Natural Language Processing (NLP) models. We apply the self-attention mechanism of Transformer to model complex dependencies within the netlist. This is the first application of large-scale pre- trained models for gate-level netlists HT detection, promoting the use of pre-trained models in the security field. Experiments on the Trust-Hub, TRIT-TC, and TRIT-TS benchmarks demonstrate that our approach outperforms existing HT detection methods. The precision increased by at least 5.27%, The True Positive Rate (TPR) by 3.06%, the True Negative Rate (TNR) by 0.01%, and the F1 score increased by about 3.17%, setting a new state-of-the-art in HT detection.