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			<titleStmt><title level='a'>Early Detection of Hardware Trojans Using Neural Controlled Differential Equations and Analysis of Power Traces</title></titleStmt>
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				<publisher>IEEE</publisher>
				<date>04/10/2026</date>
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				<bibl> 
					<idno type="par_id">10690241</idno>
					<idno type="doi">10.1109/DCAS69364.2026.11544625</idno>
					
					<author>Hasala Senevirathne</author><author>Rahul Vishwakarma</author><author>Amin Rezaei</author>
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			<abstract><ab><![CDATA[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.]]></ab></abstract>
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