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Reseach Article

A Propose Neuro-Fuzzy-Genetic Intrusion Detection System

by Ibrahim Goni, Ahmed Lawal
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 115 - Number 8
Year of Publication: 2015
Authors: Ibrahim Goni, Ahmed Lawal
10.5120/20169-2320

Ibrahim Goni, Ahmed Lawal . A Propose Neuro-Fuzzy-Genetic Intrusion Detection System. International Journal of Computer Applications. 115, 8 ( April 2015), 5-9. DOI=10.5120/20169-2320

@article{ 10.5120/20169-2320,
author = { Ibrahim Goni, Ahmed Lawal },
title = { A Propose Neuro-Fuzzy-Genetic Intrusion Detection System },
journal = { International Journal of Computer Applications },
issue_date = { April 2015 },
volume = { 115 },
number = { 8 },
month = { April },
year = { 2015 },
issn = { 0975-8887 },
pages = { 5-9 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume115/number8/20169-2320/ },
doi = { 10.5120/20169-2320 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:54:14.166331+05:30
%A Ibrahim Goni
%A Ahmed Lawal
%T A Propose Neuro-Fuzzy-Genetic Intrusion Detection System
%J International Journal of Computer Applications
%@ 0975-8887
%V 115
%N 8
%P 5-9
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The exponential growth and development of the internet has created many problems on network security. Current intrusion detection system has failed to fully protect system against sophisticated attacks. This research work explores some dedicated methodologies such as Artificial Neural Network (ANN), Fuzzy Logic, and Genetic Algorithms applied to Intrusion Detection Systems but attacks against networks and information systems are still successful. We proposed Neuro-fuzzy Genetic Intrusion Detection System which is a fusion of the three Artificial Intelligence techniques. We foresee they would stand a fighting chance against any sophisticated attack, improve accuracy, precision rate and reduce the false positive rate and would protect data integrity, confidentiality and availability. We also discuss the dataset for evaluating the system. In this work we have identified a new research direction in the related field.

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Index Terms

Computer Science
Information Sciences

Keywords

Neuro-fuzzy Genetic algorithm Artificial Neural Network Fuzzy logic intrusion detection system and Dataset.