GBiL: A hybrid gated recurrent units (GRU) and bidirectional long short-term memory (BiLSTM) model with Particle Swarm Optimization for a Robust VANET IDS

Vehicular ad hoc networks (VANET) are revolutionizing transportation by enabling real-time communication between vehicles and roadside infrastructure, enhancing safety and efficiency through the exchange of traffic updates, road conditions, and critical data. However, VANET faces significant security threats, including Sybil, black hole, and wormhole attacks, where malicious nodes manipulate network communication, leading to misinformation and disruptions. This research proposes a novel hybrid model, GBiL, integrating gated recurrent unit (GRU) and bidirectional long short-term memory (BiLSTM) to detect and mitigate such attacks. At the core of this architecture, an intrusion detection system (IDS) is combined with a trust detection module to assess the trustworthiness of network nodes using real-time data. The IDS employs a hybrid approach for efficient intrusion detection, leveraging particle swarm optimization (PSO) after feature selection. To ensure a balanced dataset, data augmentation is applied using SMOTETomek, a combination of Synthetic Minority Over-sampling Technique (SMOTE) and Tomek Links. Real-time simulations using NS-3 and SUMO with real-world mapping from OpenStreetMap validate the system’s effectiveness in a realistic urban network environment by generating a dataset called VANET Attacks (VA) dataset. This comprehensive approach strengthens VANET security against multiple attack vectors. The proposed GBiL model achieves high performance, with an accuracy of 96.01% and a false alarm rate of just 0.04%. This research significantly enhances VANET security by integrating sophisticated detection techniques, data augmentation, and real-time trust evaluations, establishing a robust foundation for more secure and reliable autonomous transportation through improved vehicular communication networks.