Deep Reinforcement Learning Based Dynamic Reputation Policy in 5G Based Vehicular Communication Networks
Sohan Gyawali, Yi Qian, Rose Qingyang Hu
Abstract
Vehicular networks are vulnerable to various attacks from malicious vehicles or infrastructures within a network. The collaborative misbehavior detection system can be used to detect these internal or insider attacks. However, in a collaborative misbehavior detection system, an attacker may lower the detection accuracy by sending false feedback. A trust model can be used to stimulate vehicles to send true feedbacks. However, an attacker can take advantage of weak or strong reputation update methods. A dynamic trust or reputation update policy can be used to stimulate vehicles to send true feedbacks. In this paper, we propose a deep reinforcement learning based dynamic reputation update policy. In the proposed scheme, feedbacks from vehicles are combined in vehicular edge computing (VEC) servers using Dempster-Shafer theory and the results are used to predict the average number of true messages. VEC then uses deep reinforcement learning to determine the optimum reputation update policy to stimulate vehicles to send true feedbacks. In addition, through extensive simulations, we show that the proposed dynamic reputation-policy is better in terms of the average number of true feedbacks compared to the existing reputation update policy.