Accurate Hardware Trojan Detection for SGIN Device: A Prompt-Tuning and LangChain Approach

Abstract

Space-Ground Integrated Networks (SGIN) devices are at risk of hardware Trojan attacks. Currently, existing detection schemes (e.g., deep learning) require a large amount of labeled samples. However, obtaining a high-quality labeled hardware Trojan dataset for SGIN devices is challenging due to the structural complexity of hardware, resulting in poor detection performance. To address this challenge, this paper combines prompt-tuning with LangChain to propose a hardware Trojan detection scheme for SGIN devices without requiring extensive training samples. In our scheme, we transform hardware Trojan detection into a mask prediction problem and design a two-phase prompt-based detection framework. In the first phase, we design 5 prompt patterns with masks and utilize Roberta-large as a large language model (LLM) to predict masks and their confidence. If their confidence is below a given threshold value, the second phase is initiated, where the corresponding original samples are fed into LangChain to optimize detection. To enhance the detection accuracy, we develop a Positional State Tree (PST) to extract the logical parallel structure of SGIN Trojan. Experiments show that our scheme achieves an accuracy of 91.3% in detecting the presence of Trojans and 96.3% in identifying the types of Trojans, respectively.