Towards Feature Selection for Detection of DDoS Attack

Abstract

Due to the rapid use of the internet, the Distributed Denial of Service (DDoS) attack is affected by E-Commerce, government, and private IT infrastructure. Intrusion Detection System is the best way to deal with the detection of DDoS attacks. In this paper, we focused on the feature selection process to improve the performance by the selection of important features. Information Gain with Ranker algorithm is used for the feature selection process. After the feature selection process, the proposed system uses Random Forest, J48, LMT (Logistic Model Tree) classifiers for the detection of the DDoS attack. The proposed system is tested with the help of CICIDS2017 dataset. The experimentation result shows that J48 classifier provides improved detection rate as compared to Random Forest and LMT with important features.