Enhancing VANET Security Using a Hybrid Model of Deep Learning and Homomorphic Encryption

ABSTRACT Vehicular Ad Hoc Networks (VANETs) play a pivotal role in enabling intelligent transportation systems, yet their decentralized and dynamic nature exposes them to a wide range of cyber threats, including Sybil attacks, black hole attacks, replay, and message spoofing. To address these vulnerabilities, we propose HyDra‐VANET, a novel hybrid security framework that integrates deep learning, federated learning, and homomorphic encryption for robust and privacy‐preserving intrusion detection. At the vehicle level, a convolutional–recurrent neural network (CRNN) is employed to extract both spatial and temporal patterns from real‐time vehicular communication and telemetry data, ensuring accurate anomaly detection. Federated learning coordinates decentralized model training across vehicles, enabling collaborative intelligence while eliminating the need to share raw data. To further enhance privacy, a lightweight lattice‐based homomorphic encryption scheme allows encrypted inference and secure aggregation, preventing sensitive information leakage at intermediate nodes such as roadside units. Experimental evaluation using multiple datasets and adversarial scenarios demonstrates that HyDra‐VANET significantly outperforms baseline intrusion detection systems in detection accuracy, resilience to adversarial manipulation, scalability, and communication efficiency.