TY - GEN
T1 - Embracing graph neural networks for hardware security
AU - Alrahis, Lilas
AU - Patnaik, Satwik
AU - Shafique, Muhammad
AU - Sinanoglu, Ozgur
N1 - Publisher Copyright:
© 2022 Copyright held by the owner/author(s).
PY - 2022/10/30
Y1 - 2022/10/30
N2 - 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.
AB - 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.
KW - Graph neural networks
KW - Hardware Trojans
KW - Hardware security
KW - Intellectual property piracy
KW - Logic locking
KW - Reverse engineering
KW - Survey
UR - http://www.scopus.com/inward/record.url?scp=85143715222&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85143715222&partnerID=8YFLogxK
U2 - 10.1145/3508352.3561096
DO - 10.1145/3508352.3561096
M3 - Conference contribution
AN - SCOPUS:85143715222
T3 - IEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD
BT - Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 41st IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2022
Y2 - 30 October 2022 through 4 November 2022
ER -