An ensemble approach for feature selection of Cyber Attack Dataset
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.
Keywords
Cite
@article{arxiv.0912.1014,
title = {An ensemble approach for feature selection of Cyber Attack Dataset},
author = {Shailendra Singh and Sanjay Silakari},
journal= {arXiv preprint arXiv:0912.1014},
year = {2009}
}
Comments
6 pages IEEE format, International Journal of Computer Science and Information Security, IJCSIS November 2009, ISSN 1947 5500, http://sites.google.com/site/ijcsis/