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Reseach Article

Hybrid Intrusion Detection System using FCRM Mechanism

by P.ananthi, P.balasubramanie
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 105 - Number 9
Year of Publication: 2014
Authors: P.ananthi, P.balasubramanie

P.ananthi, P.balasubramanie . Hybrid Intrusion Detection System using FCRM Mechanism. International Journal of Computer Applications. 105, 9 ( November 2014), 25-29. DOI=10.5120/18406-9676

@article{ 10.5120/18406-9676,
author = { P.ananthi, P.balasubramanie },
title = { Hybrid Intrusion Detection System using FCRM Mechanism },
journal = { International Journal of Computer Applications },
issue_date = { November 2014 },
volume = { 105 },
number = { 9 },
month = { November },
year = { 2014 },
issn = { 0975-8887 },
pages = { 25-29 },
numpages = {9},
url = { },
doi = { 10.5120/18406-9676 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T22:37:17.069499+05:30
%A P.ananthi
%A P.balasubramanie
%T Hybrid Intrusion Detection System using FCRM Mechanism
%J International Journal of Computer Applications
%@ 0975-8887
%V 105
%N 9
%P 25-29
%D 2014
%I Foundation of Computer Science (FCS), NY, USA

The necessity of efficient intrusion detection system increased recent research to be focused on hybrid techniques for better results. In recent research plenty of intrusion detection systems have been proposed with various data mining techniques, machine learning mechanisms and fuzzy logic. Existing intrusion detection systems suffered from higher false positive rate and negative rate. This paper proposes the integrated approach such as clustering with Fuzzy neural network for efficient detection rate. In this proposed approach, Fuzzy C-Regression technique is used to construct different training subsets. Then, FNN model is used to take decision making. This proposed approach significantly reduces the false positive and negative rate.

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Index Terms

Computer Science
Information Sciences


Intrusion Detection System Fuzzy Neural Network Fuzzy C-Regression model false positive