Hardware Trojan Detection in Open-Source Hardware Designs Using Machine Learning
Victor Takashi Hayashi, Wilson Vicente Ruggiero
Abstract
The globalization of the hardware supply chain reduces costs but increases security challenges with the potential insertion of hardware trojans by third parties. Traditional detection methods face scalability limitations by relying solely on simple examples (e.g., AES). Although open-source hardware promotes transparency, it does not guarantee security. In this research, Natural Language Processing (NLP) and Machine Learning (ML) techniques were applied to identify hardware trojans in complex open hardware designs (e.g., RISC-V, MIPS). Using data from existing benchmarks (ISCAS85-89, TrustHub) and synthetic data generated with Large Language Models (LLM), a dataset of 3,808 instances was used in this research. The approach using TF-IDF and Decision Tree (DT) achieved 97.26%, surpassing the state of the art. The use of LLMs with prompt optimization achieved a recall of 99%, minimizing false negatives. A novel framework integrating NLP, ML, and LLMs was developed to enhance the security of open-source hardware.