Analysis of Intelligent Classifiers and Enhancing the Detection Accuracy for Intrusion Detection System

Hdl Handle:
http://hdl.handle.net/10149/578691
Title:
Analysis of Intelligent Classifiers and Enhancing the Detection Accuracy for Intrusion Detection System
Authors:
Albayati, M. (Mohanad); Issac, B. (Biju)
Affiliation:
Teesside University. Digital Futures Institute
Citation:
Albayati, M., Issac, B. (2015) 'Analysis of Intelligent Classifiers and Enhancing the Detection Accuracy for Intrusion Detection System' International Journal of Computational Intelligence Systems; Published online: 21 Sep 2015
Publisher:
Taylor & Francis
Journal:
International Journal of Computational Intelligence Systems
Issue Date:
21-Sep-2015
URI:
http://hdl.handle.net/10149/578691
DOI:
10.1080/18756891.2015.1084705
Additional Links:
http://www.tandfonline.com/doi/full/10.1080/18756891.2015.1084705
Abstract:
In this paper we discuss and analyze some of the intelligent classifiers which allows for automatic detection and classification of networks attacks for any intrusion detection system. We will proceed initially with their analysis using the WEKA software to work with the classifiers on a well-known IDS (Intrusion Detection Systems) dataset like NSL-KDD dataset. The NSL-KDD dataset of network attacks was created in a military network by MIT Lincoln Labs. Then we will discuss and experiment some of the hybrid AI (Artificial Intelligence) classifiers that can be used for IDS, and finally we developed a Java software with three most efficient classifiers and compared it with other options. The outputs would show the detection accuracy and efficiency of the single and combined classifiers used.
Type:
Article
Language:
en
ISSN:
1875-6891; 1875-6883
Rights:
Following 12 month embargo author can archive post-print (ie final draft post-refereeing). For full details see http://www.sherpa.ac.uk/romeo [Accessed: 24/09/2015]

Full metadata record

DC FieldValue Language
dc.contributor.authorAlbayati, M. (Mohanad)en
dc.contributor.authorIssac, B. (Biju)en
dc.date.accessioned2015-09-24T15:49:53Zen
dc.date.available2015-09-24T15:49:53Zen
dc.date.issued2015-09-21en
dc.identifier.citationInternational Journal of Computational Intelligence Systems; Published online: 21 Sep 2015en
dc.identifier.issn1875-6891en
dc.identifier.issn1875-6883en
dc.identifier.doi10.1080/18756891.2015.1084705en
dc.identifier.urihttp://hdl.handle.net/10149/578691en
dc.description.abstractIn this paper we discuss and analyze some of the intelligent classifiers which allows for automatic detection and classification of networks attacks for any intrusion detection system. We will proceed initially with their analysis using the WEKA software to work with the classifiers on a well-known IDS (Intrusion Detection Systems) dataset like NSL-KDD dataset. The NSL-KDD dataset of network attacks was created in a military network by MIT Lincoln Labs. Then we will discuss and experiment some of the hybrid AI (Artificial Intelligence) classifiers that can be used for IDS, and finally we developed a Java software with three most efficient classifiers and compared it with other options. The outputs would show the detection accuracy and efficiency of the single and combined classifiers used.en
dc.language.isoenen
dc.publisherTaylor & Francisen
dc.relation.urlhttp://www.tandfonline.com/doi/full/10.1080/18756891.2015.1084705en
dc.rightsFollowing 12 month embargo author can archive post-print (ie final draft post-refereeing). For full details see http://www.sherpa.ac.uk/romeo [Accessed: 24/09/2015]en
dc.titleAnalysis of Intelligent Classifiers and Enhancing the Detection Accuracy for Intrusion Detection Systemen
dc.typeArticleen
dc.contributor.departmentTeesside University. Digital Futures Instituteen
dc.identifier.journalInternational Journal of Computational Intelligence Systemsen
or.citation.harvardAlbayati, M., Issac, B. (2015) 'Analysis of Intelligent Classifiers and Enhancing the Detection Accuracy for Intrusion Detection System' International Journal of Computational Intelligence Systems; Published online: 21 Sep 2015en
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