A Multi-Level Dempster-Shafer and Reinforcement Learning-Based Reputation System for Connected Vehicle Security

Data falsification attack in connected vehicles (CV) refer to the manipulation or alteration of data within the vehicle's communication systems. This paper discusses the critical challenges in ensuring the security of CV networks where vehicle data integrity is paramount to prevent data falsification. Various existing solutions, such as machine learning and reputation-based approaches, have limitations in terms of scalability and robustness. To address these issues, we propose a novel multi-level Dempster-Shafer with reinforcement learning (RL)-based reputation system for CV networks. We use decentralized validation that combines self and peer reports of vehicles along with centralized feedback from road side unit, merging reputation-based trust management with Deep RL. By incorporating a multi-level Dempster-Shafer model, we elevate prediction accuracy and reward values while dynamic RL optimizes the process of reputation updates.