An ensemble-based approach for effective distributed denial of service attack detection in software defined networking

Abstract

Software defined networking (SDN) is a network framework that aims to redefine network characteristics through the programmability of network components, faster and larger network monitoring, centralized network operation, and effective detection of fraudulent traffic and special malfunctions. However, SDN networks are vulnerable to security threats that can cause complete network failure. To address this issue, in this paper, machine learning techniques are suggested for the swift detection of attacks. Various methods for detecting distributed denial of service (DDoS) attacks are evaluated, and the study identifies the most precise method for categorizing such attacks within a SDN network. The results indicate that the proposed system achieves high accuracy in detecting DDoS attacks, with ensemble learning achieving 99% accuracy. This indicates a remarkable improvement percentage in comparison to the approaches of decision tree (DT), k-nearest neighbors (KNN), and support vector machine (SVM).