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Network Intrusion Detection Systems based Neural Network: A Comparative Study

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Authors:
Berlin H. Lekagning Djionang, Gilbert Tindo
10.5120/ijca2017912717

Berlin Lekagning H Djionang and Gilbert Tindo. Network Intrusion Detection Systems based Neural Network: A Comparative Study. International Journal of Computer Applications 157(5):42-47, January 2017. BibTeX

@article{10.5120/ijca2017912717,
	author = {Berlin H. Lekagning Djionang and Gilbert Tindo},
	title = {Network Intrusion Detection Systems based Neural Network: A Comparative Study},
	journal = {International Journal of Computer Applications},
	issue_date = {January 2017},
	volume = {157},
	number = {5},
	month = {Jan},
	year = {2017},
	issn = {0975-8887},
	pages = {42-47},
	numpages = {6},
	url = {http://www.ijcaonline.org/archives/volume157/number5/26830-2017912717},
	doi = {10.5120/ijca2017912717},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Neural networks are artificial learning systems. For more than two decades, they have help for detecting hostile behaviors in a computer system. This review describes those systems and theirs limits. It defines and gives neural networks characteristics. It also itemizes neural networks which are used in intrusion detection systems. The state of the art on IDS made from neural networks is reviewed. In this paper, we also make a taxonomy and a comparison of neural networks intrusion detection systems. We end this review with a set of remarks and future works that can be done in order to improve the systems that have been presented. This work is the result of a meticulous scan of the literature.

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Keywords

Intrusion detecting system, NIDS, neural network, MLP