An ensemble feature reduction method for web-attack detection

Abstract

There are a large number of features present in benchmark datasets that are used to test and evaluate intrusion detection systems. However, these high dimensional datasets require more computing resources and computation time. Identification of relevant and irrelevant features in high dimensional datasets plays a vital role in intrusion detection.This study proposes an ensemble feature reduction method to identify a reduced feature subset for the classification of web-attack. The ensemble method is based on information gain, correlation, gain ratio, chi-square, and ReliefF. Further, the system uses J48 classifier with a reduced feature subset for the classification of web-attack. The implemented system is tested on the CICIDS 2017 web-attack dataset which produces prominent results in terms of performance with reduced feature subset. Finally, the proposed method is compared with current state-of-the-art systems using J48 with 10-fold cross-validation.