Detection of DDoS Attacks using Semi-Supervised based Machine Learning Approaches

Abstract

Distributed denial of service or DDoS attack is a kind of network attack that is used to destroy the normal operations of the server and its responses. There are several approaches through which we can detect the abnormal behaviour of the network traffic. However, machine learning techniques are best used techniques for the detection and identification of DDoS attack. This attack is regarded as one of the most serious threats to the internet. Supervised machine learning algorithms rely on the availability of labelled network traffic datasets. Attacks, on the other hand, are identified in unsupervised approaches by analysing incoming network traffic. These techniques face challenges like less accuracy, classification issue, falser positive rate and more network data. The semi supervised ML approaches are presented in this work for the detection of DDoS which is based on the estimation of network entropy, information gain ratio, co clustering as well as Random Forest algorithm. The irrelevant normal traffic data is reduced with the unsupervised part of this approach for the detection of DDoS which also allow the reduction of false positive rates and also the accuracy is increased. On the other hand, further reduction of false positive rates of unsupervised part is done by the part which is supervised and the DDoS traffic is also accurately classified.