Deep Belief Networks for Feature Learning in VANET Security Analysis

Abstract Vehicular ad hoc networks (VANETs) play a critical role in enabling intelligent transportation systems, but their open and dynamic nature also makes them highly vulnerable to security threats. Traditional rule-based security mechanisms often fall short of identifying complex or evolving attack patterns. This study explores the application of Deep Belief Networks (DBNs) for automated feature learning and anomaly detection in VANET environments. By leveraging the hierarchical structure of DBNs, the model is trained to extract meaningful temporal and spatial features from high-dimensional traffic and communication data. These learned representations are then used to identify potential malicious behaviors such as spoofing, Sybil attacks, and false message injections. Experimental evaluations on benchmark VANET datasets demonstrate that DBNs significantly improve detection accuracy while reducing false positives compared to shallow learning approaches. This work highlights the potential of deep learning-based models in building more adaptive and resilient VANET security frameworks.