Towards the selection of best neural network system for intrusion detection

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, that is, 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. Key words: Neural networks (NN), multi-criteria decision making (MCDM), intrusion detection system (IDS), analytic hierarchy process (AHP).