A Survey on the Design, Detection, and Prevention of Pre-Silicon Hardware Trojans

Cruz, Jonathan; Hamlet, Jason

doi:10.1109/access.2025.3555166

A Survey on the Design, Detection, and Prevention of Pre-Silicon Hardware Trojans

Journal Article · Wed Mar 26 00:00:00 EDT 2025 · IEEE Access

DOI:https://doi.org/10.1109/access.2025.3555166· OSTI ID:2554136

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Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

The complexity of the semiconductor design lifecycle and globalized manufacturing process creates concern over the threat of deliberate malicious alterations, or hardware Trojans, being inserted into microelectronic designs. This has resulted in a significant corpus of hardware Trojan research including Trojan design and benchmarking efforts and development of corresponding metrics and detection and prevention techniques, over the last two decades. In this survey, we first highlight efforts in Trojan design and benchmarking, followed by a cataloging of seminal and recent works in Trojan detection and prevention and their accompanied metrics. Given the volume of literature in this field, this survey considers only pre-silicon techniques. We make this distinction between pre- and post-silicon to properly scope and provide appropriate context into the capabilities of existing hardware Trojan literature. Each major section (design, prevention, and detection) is accompanied by insights, and common pitfalls, which we highlight can be addressed by future research.

View Accepted Manuscript (DOE)

Research Organization:: Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: NA0003525

OSTI ID:: 2554136

Report Number(s):: SAND--2025-04358J

Journal Information:: IEEE Access, Journal Name: IEEE Access Vol. 13; ISSN 2169-3536

Publisher:: IEEECopyright Statement

Country of Publication:: United States

Language:: English

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Hardware Trojan Insertion Using Reinforcement Learning Sarihi, Amin; Patooghy, Ahmad; Jamieson, Peter Proceedings of the Great Lakes Symposium on VLSI 2022 https://doi.org/10.1145/3526241.3530379	conference	June 2022
The Improved COTD Technique for Hardware Trojan Detection in Gate-level Netlist Salmani, Hassan Proceedings of the Great Lakes Symposium on VLSI 2022 https://doi.org/10.1145/3526241.3530835	conference	June 2022
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A Symbolic Approach to Detecting Hardware Trojans Triggered by Don’t Care Transitions Dai, Ruochen; Yavuz, Tuba ACM Transactions on Design Automation of Electronic Systems, Vol. 28, Issue 2 https://doi.org/10.1145/3558392	journal	December 2022
Security Closure of IC Layouts Against Hardware Trojans Wang, Fangzhou; Wang, Qijing; Fu, Bangqi Proceedings of the 2023 International Symposium on Physical Design https://doi.org/10.1145/3569052.3571878	conference	March 2023
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MuxLink: Circumventing Learning-Resilient MUX-Locking Using Graph Neural Network-based Link Prediction Alrahis, Lilas; Patnaik, Satwik; Shafique, Muhammad 2022 Design, Automation & Test in Europe Conference & Exhibition (DATE) https://doi.org/10.23919/DATE54114.2022.9774603	conference	March 2022
Inter-IP Malicious Modification Detection through Static Information Flow Tracking Liu, Zhaoxiang; Arias, Orlando; Fu, Weimin 2022 Design, Automation & Test in Europe Conference & Exhibition (DATE) https://doi.org/10.23919/DATE54114.2022.9774694	conference	March 2022
DANA Universal Dataflow Analysis for Gate-Level Netlist Reverse Engineering Albartus, Nils; Hoffmann, Max; Temme, Sebastian IACR Transactions on Cryptographic Hardware and Embedded Systems https://doi.org/10.46586/tches.v2020.i4.309-336	journal	August 2020

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