GNN-RL: Dynamic Reward Mechanism for Connected Vehicle Security using Graph Neural Networks and Reinforcement Learning

Abstract

This paper introduces a new approach to incentivise the vehicles in connected vehicle (CV) networks based on the reputation measures along with a combination of graph neural network and reinforcement learning (GNN-RL). The proposed method enables vehicles to create reputation estimates of their nearby vehicles by analyzing broadcasted kinematic data and onboard sensor estimates, as well as the network connectivity topology. This data is then utilized to create a graphical representation of reputation distribution. A centralized RL agent is used for providing reward signals to each vehicle based on a Laplacian matrix, which encourages the vehicles to make more accurate reputation estimates. The proposed algorithm is based on a GNN-RL algorithm previously used for coordinated navigation, which has been adapted to the cybersecurity domain in this paper. The simulation results show that the model was effective in giving dynamic rewards to vehicles based on their reputation scores. Further, Laplace matrices helped in analyzing the connectivity and behavior of CVs in the network.