Early Detection of Hardware Trojans Using Neural Controlled Differential Equations and Analysis of Power Traces

• URL: http://arxiv.org/abs/2607.23417v1
• Date: Sun, 26 Jul 2026 02:27:59 GMT
• Title: Early Detection of Hardware Trojans Using Neural Controlled Differential Equations and Analysis of Power Traces
• Abstract summary: Evolving Hardware Trojans pose a serious threat to modern digital systems by evading traditional detection.<n>We propose a novel approach for hardware Trojan detection and prediction using Neural Controlled Differential Equations (NCDEs) and analysis of power traces.
• Score: 1.2234742322758416
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Evolving Hardware Trojans pose a serious threat to modern digital systems by evading traditional detection through stealthy, adaptive behavior. Even recent methods that leverage advances in machine learning can only detect them after activation, leaving a critical window for potential security breaches. To address this gap, we propose a novel approach for hardware Trojan detection and prediction using Neural Controlled Differential Equations (NCDEs) and analysis of power traces. Our method leverages an NCDE model trained exclusively on Trojan-free data to learn nominal power behavior, combined with a Linear Discriminant Analysis (LDA) classifier calibrated on labeled data, to distinguish between three scenarios: no Trojan, dormant Trojan, and active Trojan. Our method uses a sliding window to process side-channel measurements, enabling detection of subtle power consumption deviations that indicate Trojan presence, even when dormant. Experimental results demonstrate that the proposed NCDE-based method achieves superior accuracy compared to traditional machine learning approaches, with the additional advantage of handling dormant Trojans above a sensitivity threshold. We validate our approach on standard hardware Trojan benchmarks, showing robust detection and prediction performance.

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