DDoS SourceTracer: An Intelligent Application for DDoS Attack Mitigation in SDN

Abstract

Software-Defined Networking (SDN) enhances network management and efficiency and is particularly effective in defending against Distributed Denial of Service (DDoS) attack through its centralized structure. Our proposed DDoS SourceTracer Application utilizes SDN to efficiently identify and mitigate DDoS attack by employing tracebacking and clustering techniques. This application uses supervised and ensemble machine learning algorithms for attack detection, and feature selection methods like the Chi-square test, ANOVA (Analysis of Variance) F-test, Correlation matrix, and Extra tree classifier to optimize the feature set. Our results show that the clustering approach outperforms traditional methods like rate limiting and blocking and effectively mitigates the attack in just 3.5 s. We used the sFlow-RT tool on the Zoo topology to perform real-time analysis and validate our application’s effectiveness during attack and normal traffic. This tool analyzes how attack traffic is impacted when using clustering and tracebacking methods to mitigate DDoS attack.