GNN-RL: Dynamic Reward Mechanism for Connected Vehicle Security using Graph Neural Networks and Reinforcement Learning
Author:
Affiliation:
1. Texas State University,Department of Computer Science,San Marcos,Texas
2. San Antonio College,Department of Engineering,San Antonio,Texas
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10207555/10207557/10207565.pdf?arnumber=10207565
Reference12 articles.
1. A Data Generation Workflow for Consensus-Based Connected Vehicle Security
2. Algebraic connectivity of graphs
3. Social Psychology Inspired Distributed Ledger Technique for Anomaly Detection in Connected Vehicles
4. Handbook of Beta Distribution and Its Applications
5. Graph neural network and reinforcement learning for multi‐agent cooperative control of connected autonomous vehicles
Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Distinguishing Sensor Faults and Attacks in Connected Vehicle Networks Using Two-Step Verification;2023 Eighth International Conference On Mobile And Secure Services (MobiSecServ);2023-11-04
2. Poster: Decentralized Simulation Workflow for Enhancing Connected Vehicle Security;Proceedings of the Twenty-fourth International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing;2023-10-16
3. Leveraging Neuro-Inspired Reinforcement Learning for Secure Reputation-based Communication in Connected Vehicles;2023 IEEE Conference on Communications and Network Security (CNS);2023-10-02
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