Machine Learning-Based Classification of Hardware Trojans Using Power Side-Channel Signals

The integrity and security of integrated circuits (ICs) are crucial in the digital world, as hardware Trojans (HTs) can allow unauthorized access and cause data breaches or malfunctions. Traditional HT detection approaches, sometimes considered similar to a “golden chip”, have difficulties because of their covert nature and complicated designs. This study presents a machine learning-assisted approach for analyzing power side-channel data that overcomes these limitations. Our analysis shows the detection of HT accurately without the requirement for a golden reference by evaluating a large dataset containing different Trojan states. ML-assisted deep learning approach has significantly improved detection accuracy, providing a new direction for real-time HT monitoring and improving IC security throughout their lifecycle.