invited-talk

Embracing Graph Neural Networks for Hardware Security

Authors:

Satwik Patnaik,

Muhammad Shafique,

Ozgur SinanogluAuthors Info & Claims

ICCAD '22: Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design

Article No.: 4, Pages 1 - 9

https://doi.org/10.1145/3508352.3561096

Published: 22 December 2022 Publication History

Abstract

Graph neural networks (GNNs) have attracted increasing attention due to their superior performance in deep learning on graph-structured data. GNNs have succeeded across various domains such as social networks, chemistry, and electronic design automation (EDA). Electronic circuits have a long history of being represented as graphs, and to no surprise, GNNs have demonstrated state-of-the-art performance in solving various EDA tasks. More importantly, GNNs are now employed to address several hardware security problems, such as detecting intellectual property (IP) piracy and hardware Trojans (HTs), to name a few.

In this survey, we first provide a comprehensive overview of the usage of GNNs in hardware security and propose the first taxonomy to divide the state-of-the-art GNN-based hardware security systems into four categories: (i) HT detection systems, (ii) IP piracy detection systems, (iii) reverse engineering platforms, and (iv) attacks on logic locking. We summarize the different architectures, graph types, node features, benchmark data sets, and model evaluation of the employed GNNs. Finally, we elaborate on the lessons learned and discuss future directions.

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Cited By

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https://doi.org/10.1016/j.eswa.2025.126666
Vishwakarma RRezaei A(2025)Uncertainty-Aware Unimodal and Multimodal Learning for Evolving Hardware Trojan DetectionJournal of Hardware and Systems Security10.1007/s41635-025-00160-2Online publication date: 26-Feb-2025
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Gandhi JShekhawat DSantosh MPandey J(2024)GAN4IP: A unified GAN and logic locking-based pipeline for hardware IP securitySādhanā10.1007/s12046-024-02461-849:2Online publication date: 5-May-2024
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Embracing Graph Neural Networks for Hardware Security

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cover image ACM Conferences

ICCAD '22: Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design

October 2022

1467 pages

ISBN:9781450392174

DOI:10.1145/3508352

Conference Chair:
Tulika Mitra
National University of Singapore
,
Program Chairs:
Evangeline Young
The Chinese University of Hong Kong
,
Jinjun Xiong
University at Buffalo (UB)

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Published: 22 December 2022

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https://doi.org/10.1016/j.eswa.2025.126666
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https://doi.org/10.1007/s12046-024-02461-8
Alrahis LKnechtel JSinanoglu OTakahashi A(2023)Graph Neural NetworksProceedings of the 28th Asia and South Pacific Design Automation Conference10.1145/3566097.3568345(83-90)Online publication date: 16-Jan-2023
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Alrahis LSinanoglu O(2023)Graph Neural Networks for Hardware Vulnerability Analysis— Can you Trust your GNN?2023 IEEE 41st VLSI Test Symposium (VTS)10.1109/VTS56346.2023.10140095(1-4)Online publication date: 24-Apr-2023
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Li WPurdy RMoura JBlanton R(2023)Characterize the ability of GNNs in attacking logic locking2023 ACM/IEEE 5th Workshop on Machine Learning for CAD (MLCAD)10.1109/MLCAD58807.2023.10299817(1-6)Online publication date: 10-Sep-2023
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