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Abstract The threat of (HTs) and their detection is a widely studied field. While the effort for inserting a Trojan into an (ASIC) can be considered relatively high, especially when trusting the chip manufacturer, programmable hardware is vulnerable to Trojan insertion even after the product has been shipped or during usage. At the same time, detecting dormant HTs with small or zero-overhead triggers and payloads on these platforms is still a challenging task, as the Trojan might not get activated during the chip verification using logical testing or physical measurements. In this work, we present a novel Trojan detection approach based on a technique known from (IC) failure analysis, capable of detecting virtually all classes of dormant Trojans. Using (LLSI), we show how supply voltage modulations can awaken inactive Trojans, making them detectable using laser voltage imaging techniques. Therefore, our technique does not require triggering the Trojan. To support our claims, we present three case studies on 28 nm and 20 nm SRAM- and flash-based (FPGAs). We demonstrate how to detect with high confidence small changes in sequential and combinatorial logic as well as in the routing configuration of FPGAs in a non-invasive manner. Finally, we discuss the practical applicability of our approach on dormant analog Trojans in ASICs.
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This content will become publicly available on April 10, 2027
Early Detection of Hardware Trojans Using Neural Controlled Differential Equations and Analysis of Power Traces
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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- Award ID(s):
- 2245247
- PAR ID:
- 10690241
- Publisher / Repository:
- IEEE
- Date Published:
- ISBN:
- 979-8-3315-5193-3
- Page Range / eLocation ID:
- 1 to 6
- Format(s):
- Medium: X
- Location:
- Dallas, TX, USA
- Sponsoring Org:
- National Science Foundation
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