A Hybrid Framework for Enhancing VANET Network Security in Smart Cities Using Transfer Learning‐Based Threat Detection and Mitigation

Instantaneous interaction and autonomous modes of transport are facilitated by Vehicular Ad Hoc Networks (VANETs), which play a crucial role in the development of smart cities. However, the flexible and autonomous architecture of VANETs poses significant security risks, including susceptibility to cyberattacks, privacy breaches, and information manipulation. These challenges are exacerbated by high node mobility and the large volume of heterogeneous data exchanged in modern urban environments. This study proposes a novel security framework that leverages transfer learning‐based threat detection and mitigation techniques to address these issues. By utilizing pre‐trained machine learning models fine‐tuned with VANET‐specific datasets, the approach reduces training time and computational costs while enabling efficient detection of anomalies and attacks. The objectives are to enhance network security, safeguard data integrity, and minimize latency in threat detection processes. Research findings indicate that the proposed framework outperforms traditional machine learning models in terms of scalability, resilience, and the ability to detect malicious activities. By providing a secure communication infrastructure for VANETs, this research contributes to the development of reliable and efficient smart city systems.