DDoS Attack Detection System Using Ensemble Method Classification and Active Learning Approach

Khorram, Masoud; Rahmanimanesh, Mohammad

DDoS Attack Detection System Using Ensemble Method Classification and Active Learning Approach

Document Type : Original Article

Authors

¹ Department of Computer Engineering and Information Technology, Faculty of Electrical and Computer Engineering, Semnan University, Semnan, Iran

² Department of Computer Engineering and Information Technology, Faculty of Electrical and Computer Engineering, Semnan University, Semnan,Iran

Abstract

Distributed Denial of Service (DDoS) attack is the widespread sending of valid or invalid packets to a server on the Internet, occupying its bandwidth and preventing execute legitimate requests of other users. The best approach to secure the network from such attacks is to exploit security controls such as intrusion detection and prevention systems. Cyber security researchers have significantly focused on identifying and counteracting this attack and have increased the accuracy and performance of security systems by providing various artificial intelligence solutions. The purpose of this paper is also to provide a solution for detecting DDoS attack, where, decision tree, multi-layer perceptron and random forest classifiers have been utilized in an ensemble method to mitigate the over-fitting problem. Also, two approache, i.e., batch learning and active learning have been implemented and evaluated in the classification phase of the proposed method. The evaluation results show that the mean value of accuracy in DDoS attack detection is 99.81%.

Keywords