Detection of DDoS Attacks using Machine Learning Algorithms

Parvinder Singh Saini, Sunny Behal, Sajal Bhatia

2020127 citationsDOI

Abstract

Distributed Denial of Service attack (DDoS) is the most dangerous attack in the field of network security. DDoS attack halts normal functionality of critical services of various online applications. Systems under DDoS attacks remain busy with false requests (Bots) rather than providing services to legitimate users. These attacks are increasing day by day and have become more and more sophisticated. So, it has become difficult to detect these attacks and secure online services from these attacks. In this paper, we have used machine learning based approach to detect and classify different types of network traffic flows. The proposed approach is validated using a new dataset which is having mixture of various modern types of attacks such as HTTP flood, SID DoS and normal traffic. A machine learning tool called WEKA is used to classify various types of attacks. It has been observed that J48 algorithm produced best results as compared to Random Forest and Naïve Bayes algorithms.

Topics & Concepts

Denial-of-service attackComputer scienceC4.5 algorithmNaive Bayes classifierMachine learningAlgorithmComputer securityApplication layer DDoS attackRandom forestStatistical classificationArtificial intelligenceNetwork securitySupport vector machineThe InternetWorld Wide WebNetwork Security and Intrusion DetectionInternet Traffic Analysis and Secure E-votingAdvanced Malware Detection Techniques