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Towards the Selection of Best Neural Network System for Intrusion Detection

Currently, network security is a critical issue because a single attack can inflict catastrophic damages to computers and network systems. Various intrusion detection approaches are available to adhere to this severe issue but the dilemma is which one is more suitable. Being motivated by this situat...

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Main Authors:	Iftikhar , Ahmad, Azween, Abdullah, Abdullah , S. Alghamdi
Format:	Article
Published:	2010
Subjects:	QA75 Electronic computers. Computer science
Online Access:	http://eprints.utp.edu.my/3088/1/J5.pdf http://eprints.utp.edu.my/3088/
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id	my.utp.eprints.3088
record_format	eprints
spelling	my.utp.eprints.30882017-03-20T01:59:50Z Towards the Selection of Best Neural Network System for Intrusion Detection Iftikhar , Ahmad Azween, Abdullah Abdullah , S. Alghamdi QA75 Electronic computers. Computer science Currently, network security is a critical issue because a single attack can inflict catastrophic damages to computers and network systems. Various intrusion detection approaches are available to adhere to this severe issue but the dilemma is which one is more suitable. Being motivated by this situation, in this paper, we evaluate and compare different neural networks (NNs). The current work presents an evaluation of different neural networks such as Self-organizing map (SOM), Adaptive Resonance Theory (ART), Online Backpropagation (OBPROP), Resilient Backpropagation (RPROP) and Support Vector Machine (SVM) towards intrusion detection mechanisms using Multi-criteria Decision Making (MCDM) technique. The results indicate that in terms of performance supervised NNs are better while unsupervised NNs are better regarding training overhead and aptitude towards handling varied and coordinated intrusion. Consequently, the combined i.e. hybrid approach of NNs is the optimal solution in the area of intrusion detection. The outcome of this work may help and guide the security implementers in two possible ways, either by using the results directly obtained in this paper or by extracting the results using other similar mechanism but on different intrusion detection systems or approaches. 2010-10-04 Article NonPeerReviewed application/zip http://eprints.utp.edu.my/3088/1/J5.pdf Iftikhar , Ahmad and Azween, Abdullah and Abdullah , S. Alghamdi (2010) Towards the Selection of Best Neural Network System for Intrusion Detection. International Journal of Physical Sciences, 5 (12). pp. 1830-1839. http://eprints.utp.edu.my/3088/
institution	Universiti Teknologi Petronas
building	UTP Resource Centre
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Petronas
content_source	UTP Institutional Repository
url_provider	http://eprints.utp.edu.my/
topic	QA75 Electronic computers. Computer science
spellingShingle	QA75 Electronic computers. Computer science Iftikhar , Ahmad Azween, Abdullah Abdullah , S. Alghamdi Towards the Selection of Best Neural Network System for Intrusion Detection
description	Currently, network security is a critical issue because a single attack can inflict catastrophic damages to computers and network systems. Various intrusion detection approaches are available to adhere to this severe issue but the dilemma is which one is more suitable. Being motivated by this situation, in this paper, we evaluate and compare different neural networks (NNs). The current work presents an evaluation of different neural networks such as Self-organizing map (SOM), Adaptive Resonance Theory (ART), Online Backpropagation (OBPROP), Resilient Backpropagation (RPROP) and Support Vector Machine (SVM) towards intrusion detection mechanisms using Multi-criteria Decision Making (MCDM) technique. The results indicate that in terms of performance supervised NNs are better while unsupervised NNs are better regarding training overhead and aptitude towards handling varied and coordinated intrusion. Consequently, the combined i.e. hybrid approach of NNs is the optimal solution in the area of intrusion detection. The outcome of this work may help and guide the security implementers in two possible ways, either by using the results directly obtained in this paper or by extracting the results using other similar mechanism but on different intrusion detection systems or approaches.
format	Article
author	Iftikhar , Ahmad Azween, Abdullah Abdullah , S. Alghamdi
author_facet	Iftikhar , Ahmad Azween, Abdullah Abdullah , S. Alghamdi
author_sort	Iftikhar , Ahmad
title	Towards the Selection of Best Neural Network System for Intrusion Detection
title_short	Towards the Selection of Best Neural Network System for Intrusion Detection
title_full	Towards the Selection of Best Neural Network System for Intrusion Detection
title_fullStr	Towards the Selection of Best Neural Network System for Intrusion Detection
title_full_unstemmed	Towards the Selection of Best Neural Network System for Intrusion Detection
title_sort	towards the selection of best neural network system for intrusion detection
publishDate	2010
url	http://eprints.utp.edu.my/3088/1/J5.pdf http://eprints.utp.edu.my/3088/
_version_	1738655243601707008
score	13.211869

Towards the Selection of Best Neural Network System for Intrusion Detection

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