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Towards the Selection of Best Neural Network System for Intrusion Detection

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.

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Abstract

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.

Item Type:Article
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Academic Subject Three:petroleum engineering
Departments / MOR / COE:Research Institutes > Megacities
ID Code:3088
Deposited By: Assoc Prof Dr Azween Abdullah
Deposited On:16 Feb 2011 10:25
Last Modified:20 Mar 2017 01:59

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