DDoS Attack Detection in SDN using ML Techniques

Abstract

Abstract: The increasing prevalence of DDoS attacks poses a serious threat to modern network infrastructures. SDN has been proposed as a promising solution for enhancing network security. However, detecting and mitigating DDoS attack in software definednetwork remains a challenging task. In this research paper, suggest an innovative approach in order to identify DDoS assaults in software-defined networks using (ML) techniques. Ourmethod entails gathering and analyzing network data. Traffic data using SDN controllers. We use variety of ML techniques analyze the traffic information to discover unexpected traffic patterns that might point to the presence of a DDoS attack. Random Forest, DecisionTree, K-Means clustering are among the algorithms used. We evaluate our approach using a real-world dataset and compare it to existing DDoS detection techniques in SDN. Our results show that our approach achieves high accuracy, precision, and recall rates indetecting DDoS attacks. We also demonstrate that our technique can detect either known and unexpected DDoS assaults with low false-positive rates. Overall, our study indicates thepotency of applying machine learning methods to SDN DDoS attack detection. Our methodoffers a promising remedy for boosting network security in contemporary infrastructures.