Machine Learning Approach of Hybrid KSVN Algorithm to Detect DDoS Attack in VANET

Most of the self-driving vehicles are suspect of the of the different types attacks due to its communication pattern and changing network topology characteristics, these types of vehicles are dependent on external communication sources of VANET, which is a vehicular network, It has attracted great interest of industry and academia, but it is having a number of issues like security, traffic congestion, road safety which are not addressed properly in recent years. To address these issues it’s required to build secure framework for the communication system in VANET and to detect different types of attack are the most important needs of the network security, which has been studied adequately by many researchers. However to improve the performance and to adapt the scenario of VANET, here in this paper we proposed a novel Hybrid KSVM scheme which is based on KNN and SVM algorithm to build a secure framework to detect Distributed Daniel of Service attack which is the part of Machine Learning approach. The experimental results shows that this approach gives the better results as compared to different Machine Learning based Algorithms to detect Distributed Daniel of Service attack.

@article{Kadam2021,
  title     = {Machine Learning Approach of Hybrid KSVN Algorithm to Detect DDoS Attack in VANET},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {12},
  number    = {7},
  year      = {2021},
  publisher = {The Science and Information Organization},
  author    = {Nivedita Kadam and Krovi Raja Sekhar},
  doi       = {10.14569/IJACSA.2021.0120782},
  url       = {https://doi.org/10.14569/IJACSA.2021.0120782}
}