relation: https://khub.utp.edu.my/scholars/7879/
title: Ant colony optimization and feature selection for intrusion detection
creator: Mehmod, T.
creator: Rais, H.B.M.
description: Network intrusion detection gained a lot of attention from the security expert. Intrusion detection system has been designed for the purpose detecting attack and comprises of detection method that can be anomaly based or it can be signature based. These detection method, however, highly depends on the quality of the input features. Supervised learning approach for the detection method finds the relationship between the feature and its class. Therefore, irrelevant, redundant, and noisy features must be eliminated before applying supervised algorithm. This can be done by feature selection method. In this paper ant colony optimization has been applied for feature selection on KDD99 dataset. The reduced dataset is validated using support vector machine. Results show that accuracy of the SVM is significantly improved with reduced feature set. © Springer International Publishing Switzerland 2016.
publisher: Springer Verlag
date: 2016
type: Article
type: PeerReviewed
identifier:   Mehmod, T. and Rais, H.B.M.  (2016) Ant colony optimization and feature selection for intrusion detection.  Lecture Notes in Electrical Engineering, 387.  pp. 305-312.  ISSN 18761100     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84975887120&doi=10.1007%2f978-3-319-32213-1_27&partnerID=40&md5=31c50b4594eeada395944dbd6a4df89a
relation: 10.1007/978-3-319-32213-1₂₇
identifier: 10.1007/978-3-319-32213-1₂₇