Building an intrusion detection system using a filter-based feature selection algorithm

Ambusaidi, MA; He, X; Nanda, P; Tan, Z

Building an intrusion detection system using a filter-based feature selection algorithm

Ambusaidi, MA He, X

Nanda, P

Tan, Z

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Computers, 2016, 65 (10), pp. 2986 - 2998
Issue Date:: 2016-10-01

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Field	Value	Language
dc.contributor.author	Ambusaidi, MA	en_US
dc.contributor.author	He, X https://orcid.org/0000-0001-8962-540X	en_US
dc.contributor.author	Nanda, P https://orcid.org/0000-0002-5748-155X	en_US
dc.contributor.author	Tan, Z	en_US
dc.date.issued	2016-10-01	en_US
dc.identifier.citation	IEEE Transactions on Computers, 2016, 65 (10), pp. 2986 - 2998	en_US
dc.identifier.issn	0018-9340	en_US
dc.identifier.uri	http://hdl.handle.net/10453/41643
dc.description.abstract	© 1968-2012 IEEE. Redundant and irrelevant features in data have caused a long-term problem in network traffic classification. These features not only slow down the process of classification but also prevent a classifier from making accurate decisions, especially when coping with big data. In this paper, we propose a mutual information based algorithm that analytically selects the optimal feature for classification. This mutual information based feature selection algorithm can handle linearly and nonlinearly dependent data features. Its effectiveness is evaluated in the cases of network intrusion detection. An Intrusion Detection System (IDS), named Least Square Support Vector Machine based IDS (LSSVM-IDS), is built using the features selected by our proposed feature selection algorithm. The performance of LSSVM-IDS is evaluated using three intrusion detection evaluation datasets, namely KDD Cup 99, NSL-KDD and Kyoto 2006+ dataset. The evaluation results show that our feature selection algorithm contributes more critical features for LSSVM-IDS to achieve better accuracy and lower computational cost compared with the state-of-the-art methods.	en_US
dc.relation.ispartof	IEEE Transactions on Computers	en_US
dc.relation.isbasedon	10.1109/TC.2016.2519914	en_US
dc.subject.classification	Computer Hardware & Architecture	en_US
dc.title	Building an intrusion detection system using a filter-based feature selection algorithm	en_US
dc.type	Journal Article
utslib.citation.volume	10	en_US
utslib.citation.volume	65	en_US
utslib.for	0803 Computer Software	en_US
utslib.for	0805 Distributed Computing	en_US
utslib.for	1006 Computer Hardware	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CRIN - Realtime Information Networks
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
utslib.copyright.status	closed_access
pubs.issue	10	en_US
pubs.publication-status	Published	en_US
pubs.volume	65	en_US

Abstract:

© 1968-2012 IEEE. Redundant and irrelevant features in data have caused a long-term problem in network traffic classification. These features not only slow down the process of classification but also prevent a classifier from making accurate decisions, especially when coping with big data. In this paper, we propose a mutual information based algorithm that analytically selects the optimal feature for classification. This mutual information based feature selection algorithm can handle linearly and nonlinearly dependent data features. Its effectiveness is evaluated in the cases of network intrusion detection. An Intrusion Detection System (IDS), named Least Square Support Vector Machine based IDS (LSSVM-IDS), is built using the features selected by our proposed feature selection algorithm. The performance of LSSVM-IDS is evaluated using three intrusion detection evaluation datasets, namely KDD Cup 99, NSL-KDD and Kyoto 2006+ dataset. The evaluation results show that our feature selection algorithm contributes more critical features for LSSVM-IDS to achieve better accuracy and lower computational cost compared with the state-of-the-art methods.

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http://hdl.handle.net/10453/41643