<p>Vehicular Ad Hoc Networks (VANETs), enable smooth vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) connections, are vital to the growth of intelligent transportation systems (ITS). However, serious security threats, such as message tampering, spoofing, denial-of-service attacks, and routing interruptions are introduced by VANETs’ open and dynamic nature. Research suggests a robust context-aware security framework that integrates Traffic Pattern Analysis (TPA), Intrusion Detection Systems (IDS), and a novel deep learning (DL) approach, Weighted Golden Jackal-driven Attention Generative Adversarial Network (WGJ-Att-GAN) to address these vulnerabilities effectively. Data collection is performed employ both simulated VANET environments and real-time data streams, focusing on key contextual parameters such as vehicle speed, GPS location, direction, communication intervals, and message types. The WGJ-Att-GAN approach serves as the core detection engine, where the WGJ algorithm dynamically adjusts feature importance to enhance anomaly sensitivity. Simultaneously, the attention mechanism within the GAN focuses on critical regions in the traffic data, improving the detection of subtle and novel intrusions. Improving decision-making accuracy includes the TPA monitoring behavioral changes in traffic flows, while the IDS employs a hybrid detection strategy that blends anomaly-based and signature-based techniques. The combined framework establishes high-performance metrics, such as recall (95.60%), F1-score (97.70%), accuracy (99.45%), and precision (98.99%), significantly outperforming traditional security models. All experiments were implemented on the python platform. By including adaptive learning and contextual awareness, this framework provides an intelligent and scalable solution for safeguarding VANET communications, thereby ensuring the reliability and security of further autonomous and connected vehicular systems.</p>