🤖 AI Summary
This work proposes the first reference-free detection method for always-on hardware Trojans (HTs), addressing the challenge of identifying such malicious circuits in the absence of a golden reference model. The approach leverages multi-window short-time Fourier transform (STFT) to extract time-frequency features from electromagnetic side-channel emissions and employs Gaussian mixture models (GMMs) to capture the statistical structure of circuit behavior. Detection is achieved by exploiting the distinct statistical stability patterns in the time-frequency spectra between Trojan-free and HT-infected circuits. Experimental validation on an AES-128 implementation demonstrates that the method accurately distinguishes circuits with always-on HTs from benign ones, significantly enhancing hardware security assurance in reference-free scenarios.
📝 Abstract
Always-on hardware Trojans (HTs) pose a critical risk to trusted microelectronics, yet most side-channel detection methods rely on unavailable golden references. We present a reference-free approach that combines time-frequency EM analysis with Gaussian Mixture Models (GMMs). By applying Short-Time Fourier Transform (STFT) at multiple window sizes, we show that HT-free circuits exhibit fluctuating statistical structure, while always-on HTs leave persistent footprints with fewer, more consistent mixture components. Results on AES-128 demonstrate feasibility without requiring reference models.