Dynamic Approaches for Detection of DDoS Threats using Machine Learning

Abstract

Distributed Denial of Service (DDoS) attacks has become one of the major and fastest growing threats on the Internet. DDoS attacks are a type of cyber attack which targets a specific machine or network in an attempt to make it unavailable, unusable for a period of time. So detecting different types of DDoS cyber threats with better algorithms and higher accuracies while keeping the computational cost at check has become the most important aspect in detecting DDoS attacks. Determining the type of DDoS attack is of paramount importance in effectively defending the targeted network or the system. This paper presents several ensemble classification techniques that combine the performance of various algorithms and compares it with existing Machine Learning Algorithms in effectively detecting the types of DDoS attacks using accuracy, F1 scores and ROC curves.