Blockchain‐Based Anomaly Detection in Vehicular Ad‐Hoc Networks Using Deep Reinforcement Learning
Аннотация
ABSTRACT Utilizing blockchain in vehicular ad‐hoc networks (VANETs) can proficiently resolve concerns pertaining to data security and privacy. The limited throughput of blockchain obstructs its extensive implementation in VANETs. Current studies on enhancing blockchain throughput frequently encounter the issue of action space proliferation, leading to inadequate scalability. This research presents a strategy for optimizing blockchain performance on VANETs using deep reinforcement learning (DRL). The suggested method enhances blockchain throughput by selecting block producers and consensus methods, and by modifying block size and intervals, while maintaining decentralization, low latency, and security in VANET‐based blockchain systems. Furthermore, to augment network security, an anomaly detection mechanism is incorporated, utilizing machine learning techniques to identify and mitigate potential attacks aimed at VANETs. The proposed system enhances throughput and fortifies resilience against malicious operations by identifying anomalous patterns in network behavior. The method uses the BDQ framework to meticulously partition the action space, tackling the action space explosion issue that occurs with conventional DRL techniques in blockchain throughput optimization. Simulation results indicate that the suggested solution significantly improves the throughput and security of the VANET‐based blockchain system.