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Improvement anomaly intrusion detection using Fuzzy-ART based on K-means based on SNC Labeling

Intrusion detection has received a lot of attention from many researchers, and various techniques have been used to identify intrusions or attacks against computers and networks. Data mining is a well-known artificial intelligence technique to build network intrusion detection systems. However, nume...

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Main Authors: Zulaiha Ali Othman,, Afaf Muftah Adabashi,, Suhaila Zainudin,, Saadat M. Al Hashmi,
Format: Article
Language:English
Published: Penerbit UKM 2011
Online Access:http://journalarticle.ukm.my/6244/1/1295-2497-1-SM.pdf
http://journalarticle.ukm.my/6244/
http://ejournals.ukm.my/apjitm/issue/archive
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spelling my-ukm.journal.62442016-12-14T06:40:37Z http://journalarticle.ukm.my/6244/ Improvement anomaly intrusion detection using Fuzzy-ART based on K-means based on SNC Labeling Zulaiha Ali Othman, Afaf Muftah Adabashi, Suhaila Zainudin, Saadat M. Al Hashmi, Intrusion detection has received a lot of attention from many researchers, and various techniques have been used to identify intrusions or attacks against computers and networks. Data mining is a well-known artificial intelligence technique to build network intrusion detection systems. However, numerous data mining techniques have been successfully applied in this area to find intrusions hidden in large amounts of audit data through classification, clustering or association rule. Clustering is one of the promising techniques used in Anomaly Intrusion Detection (AID), especially when dealing with unknown patterns. This paper presents our work to improve the performance of anomaly intrusion detection using Fuzzy-ART based on the K-means algorithm. The K-means is a modified version of the standard K-means by initializing the value K from the value obtained after data mining using Fuzzy-ART and SNC labeling technique. The result has shown that this algorithm has increased the detection rate and reduced the false alarm rate compared with Fuzzy-ART. Penerbit UKM 2011-06 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/6244/1/1295-2497-1-SM.pdf Zulaiha Ali Othman, and Afaf Muftah Adabashi, and Suhaila Zainudin, and Saadat M. Al Hashmi, (2011) Improvement anomaly intrusion detection using Fuzzy-ART based on K-means based on SNC Labeling. Jurnal Teknologi Maklumat dan Multimedia, 10 . pp. 1-11. ISSN 1823-0113 http://ejournals.ukm.my/apjitm/issue/archive
institution Universiti Kebangsaan Malaysia
building Perpustakaan Tun Sri Lanang Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Kebangsaan Malaysia
content_source UKM Journal Article Repository
url_provider http://journalarticle.ukm.my/
language English
description Intrusion detection has received a lot of attention from many researchers, and various techniques have been used to identify intrusions or attacks against computers and networks. Data mining is a well-known artificial intelligence technique to build network intrusion detection systems. However, numerous data mining techniques have been successfully applied in this area to find intrusions hidden in large amounts of audit data through classification, clustering or association rule. Clustering is one of the promising techniques used in Anomaly Intrusion Detection (AID), especially when dealing with unknown patterns. This paper presents our work to improve the performance of anomaly intrusion detection using Fuzzy-ART based on the K-means algorithm. The K-means is a modified version of the standard K-means by initializing the value K from the value obtained after data mining using Fuzzy-ART and SNC labeling technique. The result has shown that this algorithm has increased the detection rate and reduced the false alarm rate compared with Fuzzy-ART.
format Article
author Zulaiha Ali Othman,
Afaf Muftah Adabashi,
Suhaila Zainudin,
Saadat M. Al Hashmi,
spellingShingle Zulaiha Ali Othman,
Afaf Muftah Adabashi,
Suhaila Zainudin,
Saadat M. Al Hashmi,
Improvement anomaly intrusion detection using Fuzzy-ART based on K-means based on SNC Labeling
author_facet Zulaiha Ali Othman,
Afaf Muftah Adabashi,
Suhaila Zainudin,
Saadat M. Al Hashmi,
author_sort Zulaiha Ali Othman,
title Improvement anomaly intrusion detection using Fuzzy-ART based on K-means based on SNC Labeling
title_short Improvement anomaly intrusion detection using Fuzzy-ART based on K-means based on SNC Labeling
title_full Improvement anomaly intrusion detection using Fuzzy-ART based on K-means based on SNC Labeling
title_fullStr Improvement anomaly intrusion detection using Fuzzy-ART based on K-means based on SNC Labeling
title_full_unstemmed Improvement anomaly intrusion detection using Fuzzy-ART based on K-means based on SNC Labeling
title_sort improvement anomaly intrusion detection using fuzzy-art based on k-means based on snc labeling
publisher Penerbit UKM
publishDate 2011
url http://journalarticle.ukm.my/6244/1/1295-2497-1-SM.pdf
http://journalarticle.ukm.my/6244/
http://ejournals.ukm.my/apjitm/issue/archive
_version_ 1643736698502250496
score 13.223943

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