A Novel Multilevel Framework for DoS Detection in SDN

DoS attacks have been the most popular type of attack on SDNs. The threat landscape has widened due to advanced persistent threats. Recent studies have focused on a single level of defence and conventional detection methods, which have become redundant. The study proposes and implements a novel multilevel DoS attack detection, which has a three-pronged approach to counter modern-day DoS attacks. The first level emphasizes the Zero Trust mechanism using Hash SHA-256 to validate the clients. The second level uses hybrid deep learning models to detect DoS attacks, which are trained and tested across three latest datasets, namely NSLKDD, CIC DOS 2019 and IOT2023, giving an accuracy of 95% consistently. The third level is a lightweight adaptive DoS detection, which can detect fast and low-rate DoS attacks, ensuring that the SDN is secure in a few milliseconds by ruling out any possibility of congestion. The results clearly indicate how a three-level approach can thwart most advanced persistent threats.

@article{Mathew2025,
  title     = {A Novel Multilevel Framework for DoS Detection in SDN},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {8},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Rejo Rajan Mathew and Amarsinh Vidhate},
  doi       = {10.14569/IJACSA.2025.0160838},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160838}
}