Machine learning based detection of DDoS attacks in software defined network - Charulatha Kannan; Rajendiran Muthusamy; Vimala Srinivasan; Vivek Chidambaram; Kiruthika Karunakaran (Institute of Advanced Engineering and Science, 2023)
Nowadays, software defined networking (SDN) offers benefits in the area so fautomation, elasticity, and resource consumption. However, evidenceis there that SDN controller may undergo certain defeat for the network structure, particularly as the yare targeted by attacks like denial of service (DoS). Due to this network traffic has increased tremendously and attacked the server severely. To handle this issue, weused the Ryu controller and Mininet tool to identify and all eviate the DoS attack by the machine learning (ML) algorithm. Since ML is deemed as themain method for detecting peculiarities, the detection of DoS attacks was done through ML based classification. In this paper, several ML techniques were used to identify the DoS attack, and the traffic which is causing the attack has been dropped immediately to avoid congestion. The proposed work hasbeen simulated in Mininet and the results show that the proposed work detects DoS attacks well and achieves good accuracy.