Embracing graph neural networks for hardware security

Lilas Alrahis, Satwik Patnaik, Muhammad Shafique, Ozgur Sinanoglu

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Graph neural networks (GNNs) have attracted increasing attention due to their superior performance in deep learning on graphstructured 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-theart 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.

Original languageEnglish (US)
Title of host publicationProceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781450392174
StatePublished - Oct 30 2022
Event41st IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2022 - San Diego, United States
Duration: Oct 30 2022Nov 4 2022

Publication series

NameIEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD
ISSN (Print)1092-3152


Conference41st IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2022
Country/TerritoryUnited States
CitySan Diego


  • Graph neural networks
  • Hardware Trojans
  • Hardware security
  • Intellectual property piracy
  • Logic locking
  • Reverse engineering
  • Survey

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design


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