eprintid: 7879
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/78/79
datestamp: 2023-11-09 16:19:43
lastmod: 2023-11-09 16:19:43
status_changed: 2023-11-09 16:10:38
type: article
metadata_visibility: show
creators_name: Mehmod, T.
creators_name: Rais, H.B.M.
title: Ant colony optimization and feature selection for intrusion detection
ispublished: pub
keywords: Ant colony optimization; Artificial intelligence; Feature extraction; Learning systems; Mercury (metal); Signal detection; Signal processing; Support vector machines, Detecting attacks; Detection methods; Feature selection methods; Intrusion Detection Systems; Network intrusion detection; Security experts; Supervised algorithm; Supervised learning approaches, Intrusion detection
note: cited By 28; Conference of International Conference on Machine Learning and Signal Processing, MALSIP 2015 ; Conference Date: 12 June 2015 Through 14 June 2015; Conference Code:176019
abstract: 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.
date: 2016
publisher: Springer Verlag
official_url: 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
id_number: 10.1007/978-3-319-32213-1₂₇
full_text_status: none
publication: Lecture Notes in Electrical Engineering
volume: 387
pagerange: 305-312
refereed: TRUE
isbn: 9783319322124
issn: 18761100
citation:   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