Golden Chip-Free Hardware Trojan Detection Using Attention-Based Non-Local Convolution With Simple Recurrent Unit

The globalization of the semiconductor industry introduces vulnerabilities like hardware Trojans that compromise hardware security and pose serious risks to critical applications such as military and medical systems. Traditional hardware Trojan detection methods including reverse engineering (destructive) and golden chip comparison (non-destructive) are often associated with high costs and limited practicality. The emergence of machine learning and deep learning models has enhanced the feasibility of hardware Trojan detection, as these models can learn complex patterns and representations from extensive datasets. This research presents an attention-based non-local convolution model integrated with a simple recurrent unit (Att_NLC-SRU) for hardware Trojan classification effectively eliminating the need for a golden chip. In this approach, a non-local convolutional neural network embedded with an attention module plays a crucial role in efficiently extracting global spatial features from relevant regions. Furthermore, temporal features are extracted using recurrent neural networks such as the simple recurrent unit. The integration of these methodologies has proven to be significantly effective. The proposed model was validated using the advanced encryption standard (AES) benchmarks, achieving the highest accuracy of 99% on the AES-T1600 benchmark.