Enhancing VANET Security: An Unsupervised Learning Approach for Mitigating False Information Attacks in VANETs

Vehicular ad hoc networks (VANETs) enable communication among vehicles and between vehicles and infrastructure to provide safety and comfort to the users. Malicious nodes in VANETs may broadcast false information to create the impression of a fake event or road congestion. In addition, several malicious nodes may collude to collectively launch a false information attack to increase the credibility of the attack. Detection of these attacks is critical to mitigate the potential risks they bring to the safety of users. Existing techniques for detecting false information attacks in VANETs use different approaches such as machine learning, blockchain, trust scores, statistical methods, etc. These techniques rely on historical information about vehicles, artificial data used to train the technique, or coordination among vehicles. To address these limitations, we propose a false information attack detection technique for VANETs using an unsupervised anomaly detection approach. The objective of the proposed technique is to detect false information attacks based on only real-time characteristics of the network, achieving high accuracy and low processing delay. The performance evaluation results show that our proposed technique offers 30% lower data processing delay and a 17% lower false positive rate compared to existing approaches in scenarios with high proportions of malicious nodes.