A Deep Learning Approach for Hardware Trojan Detection in Netlist of Integrated Circuits with Graph Neural Networks
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-3604-1_8
Reference24 articles.
1. Fu W, Yu H, Arias O, Yang K, Jin Y, Yavuz T, Guo X (2022) Graph neural network-based hardware Trojan detection at intermediate representative for SoC Platforms. In: Proceedings of the ACM great lakes symposium on VLSI, pp 481–486
2. Yasaei R, Chen L, Yu SY, Faruque M (2022) Hardware Trojan detection using graph neural networks. IEEE Trans Comput-Aided Design of Integrat Circuits and Syst 1(1)
3. Yasaei R, Yu S, Faruque MA (2021) GNN4TJ: graph neural networks for hardware Trojan detection at register transfer level. In: 2021 Design, automation and test in Europe conference and exhibition, pp 1504–1509
4. Scarselli F, Gori M, Tsoi AC, Hagenbuchner M, Monfardini G (2009) The graph neural network model. IEEE Trans Neural Networks 20(1):61–80
5. Waksman A, Suozzo M, Sethumadhavan S (2013) FANCI: identification of stealthy malicious logic using boolean functional analysis. In: Proceedings of the ACM conference on computer and communications security, pp 697–708
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