Boosted Modified Probabilistic Neural Network (BMPNN) for Network Intrusion Detection

Tran, TP; Jan, T

Boosted Modified Probabilistic Neural Network (BMPNN) for Network Intrusion Detection

Tran, TP Jan, T

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: Neural Networks, 2006. IJCNN '06. International Joint Conference on, 2006, pp. 2354 - 2361
Issue Date:: 2006-01

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Field	Value	Language
dc.contributor.author	Tran, TP	en_US
dc.contributor.author	Jan, T	en_US
dc.contributor.editor	NA	en_US
dc.date	2006-07-16	en_US
dc.date.issued	2006-01	en_US
dc.identifier.citation	Neural Networks, 2006. IJCNN '06. International Joint Conference on, 2006, pp. 2354 - 2361	en_US
dc.identifier.isbn	0-7803-9490-9	en_US
dc.identifier.uri	http://hdl.handle.net/10453/10746
dc.description.abstract	Most of the currently available network security techniques are not able to cope with the dynamic and increasingly complex nature of the attacks on distributed computer systems. An automated and adaptive defensive tool is imperative for computer networks. One of the emerging solutions for Network Security is the Intrusion Detection System (IDS). However, this technology still faces some challenges such as low detection rates, high false alarm rates and requirement of heavy computational power. To overcome these difficulties, this paper proposes an innovative Machine Learning algorithm called Boosted Modified Probabilistic Neural Network (BMPNN) which utilizes semi-parametric learning model and Adaptive boosting techniques to reduce learning bias and generalization variance in difficult classification. In this paper, BMPNN is implemented as a classifier to detect different types of network anomalies in the KDD-99 benchmark. Extensive experimental outcome indicates that the proposed BMPNN outperforms other state-of-the-art learning algorithms in terms of detection accuracy and model robustness at an affordable computational cost.	en_US
dc.publisher	IEEE	en_US
dc.relation.ispartof	Neural Networks, 2006. IJCNN '06. International Joint Conference on	en_US
dc.relation.ispartof	IEEE International Joint Conference on Neural Networks	en_US
dc.relation.isbasedon	10.1109/IJCNN.2006.247058	en_US
dc.rights	© 2006 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.title	Boosted Modified Probabilistic Neural Network (BMPNN) for Network Intrusion Detection	en_US
dc.type	Conference Proceeding
utslib.location	Piscataway, USA	en_US
utslib.location.activity	Vancouver, Canada	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Vancouver, Canada	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/Faculty of Engineering and Information Technology/School of Systems, Management and Leadership
utslib.copyright.status	closed_access
pubs.consider-herdc	false	en_US
pubs.place-of-publication	Piscataway, USA	en_US
pubs.start-date	2006-07-16	en_US

Abstract:

Most of the currently available network security techniques are not able to cope with the dynamic and increasingly complex nature of the attacks on distributed computer systems. An automated and adaptive defensive tool is imperative for computer networks. One of the emerging solutions for Network Security is the Intrusion Detection System (IDS). However, this technology still faces some challenges such as low detection rates, high false alarm rates and requirement of heavy computational power. To overcome these difficulties, this paper proposes an innovative Machine Learning algorithm called Boosted Modified Probabilistic Neural Network (BMPNN) which utilizes semi-parametric learning model and Adaptive boosting techniques to reduce learning bias and generalization variance in difficult classification. In this paper, BMPNN is implemented as a classifier to detect different types of network anomalies in the KDD-99 benchmark. Extensive experimental outcome indicates that the proposed BMPNN outperforms other state-of-the-art learning algorithms in terms of detection accuracy and model robustness at an affordable computational cost.

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http://hdl.handle.net/10453/10746