An ensemble approach for feature selection of Cyber Attack Dataset

TL;DR

This paper presents a hybrid feature selection method combining filter and wrapper techniques, improving attack classification accuracy on the DARPA KDDCUP99 dataset.

Contribution

It introduces a novel two-phase feature selection approach that enhances attack detection performance using a hybrid filter-wrapper method.

Findings

Improved attack classification accuracy on DARPA KDDCUP99 dataset

Effective feature subset selection with high information gain

Enhanced system performance through hybrid feature selection

Abstract

Feature selection is an indispensable preprocessing step when mining huge datasets that can significantly improve the overall system performance. Therefore in this paper we focus on a hybrid approach of feature selection. This method falls into two phases. The filter phase select the features with highest information gain and guides the initialization of search process for wrapper phase whose output the final feature subset. The final feature subsets are passed through the Knearest neighbor classifier for classification of attacks. The effectiveness of this algorithm is demonstrated on DARPA KDDCUP99 cyber attack dataset.