Machine Learning Framework for Detecting False Alerts in Safety Messages

Avinash Karhana, Ikjot Saini, Arunita Jaekel

The recent advances in Vehicular Ad Hoc Networks (VANETs) can have a tremendous positive impact on vehicle safety and traffic flow. In VANETs, vehicles communicate wirelessly with each other and with roadside infrastructure nodes to improve awareness of neighboring vehicles and traffic conditions. However, such communication also increases the potential for various safety and security challenges, such as the threat of false reporting attacks. In these attacks, malicious or compromised nodes inject alert notifications that report fictitious traffic incidents that may trigger unnecessary evasive actions and increase the risk of collisions. This research addresses false alert attacks in VANETs by developing a machine learning-based detection framework that leverages innovative feature engineering and model assessment strategies. The proposed framework designs new features that capture vehicle kinematics to improve the detection of malicious alerts. The performance of multiple ML models is then analyzed in terms of both detection effectiveness and computational requirements to determine their suitability for different deployment scenarios. Our simulation results demonstrate that the proposed framework can achieve significant improvements compared to existing techniques for false alert detection. In addition, the study highlights the critical role of feature engineering in improving detection performance.