Artificial neural network approaches to intrusion detection: a review

Authors:
Iftikhar Ahmad;Azween B. Abdullah;Abdullah S. Alghamdi
Affiliations:
College of Computer and Information Sciences, King Saud University, Riyadh, Kingdom of Saudi Arabia and Department of Computer and Information Sciences, Universiti Teknologi PETRONAS, Tronoh, Pera ...;College of Computer and Information Sciences, King Saud University, Riyadh, Kingdom of Saudi Arabia and Department of Computer and Information Sciences, Universiti Teknologi PETRONAS, Tronoh, Pera ...;College of Computer and Information Sciences, King Saud University, Riyadh, Kingdom of Saudi Arabia and Department of Computer and Information Sciences, Universiti Teknologi PETRONAS, Tronoh, Pera ...
Venue:
TELE-INFO'09 Proceedings of the 8th Wseas international conference on Telecommunications and informatics
Year:
2009

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Abstract

Intrusion detection systems are the foremost tools for providing safety in computer and network system. There are many limitations in traditional IDSs like time consuming statistical analysis, regular updating, non adaptive, accuracy and flexibility. It is an Artificial Neural Network that supports an ideal specification of an Intrusion Detection System and is a solution to the problems of traditional IDSs. Therefore, An Artificial Neural Network inspired by nervous system has become an interesting tool in the applications of Intrusion Detection Systems due to its promising features. Intrusion detection by Artificial Neural Networks is an ongoing area. In this paper, we provide an introduction and review of the Artificial Neural Network Approaches within Intrusion Detection, in addition to make suggestions for future research. We also discuss on tools and datasets that are being used in Artificial Neural Network Intrusion Detection Systems. This review may help the researcher to develop new optimize approach in the field of Intrusion Detection.