Intelligent defense strategies: Comprehensive attack detection in VANET with deep reinforcement learning

Abstract

Vehicular Ad Hoc Network (VANET) facilitates the exchange of vehicular information through Vehicle-to-Vehicle (V2V) communication, contributing to Cooperative Intelligent Transportation Systems (C-ITS). The transmitted messages among vehicles are vulnerable to various security threats executed by malicious insider nodes. The dynamic VANET necessitates context-aware solutions for detecting various security attacks. Existing learning and deterministic mechanisms showed high detection accuracy for attacks on which they were trained explicitly for large datasets. Therefore, we propose an intelligent framework utilizing Deep Reinforcement Learning (DRL) for attack detection in evolving scenarios and mitigate the need for extensive training datasets. Our approach employs a Deep Q Network (DQN) trained on a compact dataset encompassing multiple attacks. The trained model is then applied to an unknown and extensive dataset, detecting various attacks with high accuracy. Notably, the model autonomously updates itself upon observing changes in the network context. This framework represents a promising security solution that is effective and adaptable for V2V communication in VANET.