Collaborative based Vehicular Ad-hoc Network Intrusion Detection System using Optimized Support Vector Machine

The Vehicular Ad hoc Network (VANET) can be used to provide secured information to the user vehicles. However, these days the immunity of safeguarding the information from vulnerabilities and threats are of great challenge. Therefore, it is necessary to provide a secured solution for the improvement of security with the deployment of advanced technology. In context with this, a blockchain based VANET structure for secured communication incorporated with the enhancement of confidentiality, scalability, and privacy is planned. The k-means clustering model forms cluster formation. The cluster head selection is carried out with Tabu Search-based Particle Swarm Optimization (TS-PSO) algorithm. The proposed approach aims to mitigate the delay with the enhancement of throughput and energy efficiency. Meanwhile, the deployed blockchain will enhance reliability and security. Moreover, the novel War Strategy Optimization (WSO) based Support Vector Machine (SVM) model (Optimized SVM) can be used for the trust-based collaborative intrusion detection in the VANET. Our work targets to detect the intrusion and non-intrusion classes. Meanwhile, our proposed work can be used for the prevention of repetitive detection processes and therein it enhances the security by rewarding the vehicles. An experimental analysis is carried out to ensure its usage in detecting the malicious node from the resource constraint vehicles and also used to achieve better security, energy utilization and end-to-end delay.

@article{M2023,
  title     = {Collaborative based Vehicular Ad-hoc Network Intrusion Detection System using Optimized Support Vector Machine},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {3},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Azath M and Vaishali Singh},
  doi       = {10.14569/IJACSA.2023.0140327},
  url       = {https://doi.org/10.14569/IJACSA.2023.0140327}
}