Distributed Dos Attacks Detection Based on Machine Learning Techniques in Software Defined Networks - Kandan, M.; Rani, P. Shobha; Sathiya, T.; Naidu, K. Bhanu Rajesh; Maheswari, M. (International Journal of Intelligent Systems and Applications in Engineering, 2024)
Manageability, scaling, and enhanced efficiency are all benefits of Software Defined Networking (SDN). However, if the controller is prone to DDoS attacks, SDN presents a unique set of security challenges. DDoS attacks have resulted in massive economic losses for civilization. They have evolved into one of the most significant challenges to Internet security. In a cloud and large data world, most existing detection approaches based on a single function and defined parameter values are unable to detect early DDoS attacks. The network connectivity and integration capacity of the SDN controller are overloaded while it is vulnerable to DDoS attacks. The high amount of flow that the controller is producing for the attack packets causes the switch flow database ability to fill up, which lowers network output to a critical threshold. Artificial Neural Network (ANN) techniques were used in this paper to detect DDoS attacks in SDN. The test findings showed that the highest accuracy rate in DDoS threat detection was achieved when an ANN classification method was combined with wrapper function selection applied. The proposed system is tested against existing benchmarks on a current state-of-the-art Flow-based dataset. The results demonstrate how Feature Selection (FS) strategies and the ANN approach may both reduce processing times and reduce processing difficulties in SDN DDoS attack detection.