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Network Intrusion Detection using Selected Data Mining Approaches: A Review

by Munawara Saiyara Munia, Samira Samrose, Pranab Dey, Afsana Salauddin Annesha, Syeda Shabnam Hasan
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 132 - Number 13
Year of Publication: 2015
Authors: Munawara Saiyara Munia, Samira Samrose, Pranab Dey, Afsana Salauddin Annesha, Syeda Shabnam Hasan
10.5120/ijca2015907572

Munawara Saiyara Munia, Samira Samrose, Pranab Dey, Afsana Salauddin Annesha, Syeda Shabnam Hasan . Network Intrusion Detection using Selected Data Mining Approaches: A Review. International Journal of Computer Applications. 132, 13 ( December 2015), 9-16. DOI=10.5120/ijca2015907572

@article{ 10.5120/ijca2015907572,
author = { Munawara Saiyara Munia, Samira Samrose, Pranab Dey, Afsana Salauddin Annesha, Syeda Shabnam Hasan },
title = { Network Intrusion Detection using Selected Data Mining Approaches: A Review },
journal = { International Journal of Computer Applications },
issue_date = { December 2015 },
volume = { 132 },
number = { 13 },
month = { December },
year = { 2015 },
issn = { 0975-8887 },
pages = { 9-16 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume132/number13/23652-2015907572/ },
doi = { 10.5120/ijca2015907572 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:29:16.063957+05:30
%A Munawara Saiyara Munia
%A Samira Samrose
%A Pranab Dey
%A Afsana Salauddin Annesha
%A Syeda Shabnam Hasan
%T Network Intrusion Detection using Selected Data Mining Approaches: A Review
%J International Journal of Computer Applications
%@ 0975-8887
%V 132
%N 13
%P 9-16
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Due to the rapid progress in network technologies, easy availability of the internet and lower cost of mobile devices with wireless network connection facility, the number of internet users is increasing at an exponential rate now-a-days, so does the number of intrusion. Despite the implausible advancement in Information Technology, Intrusion Detection has remained as one of the biggest challenges encountered by network security specialists. Data mining can play a vital role in addressing this issue. In this paper, some selected data mining algorithms available for Network Intrusion Detection have been reviewed, such as- Support Vector Machine, K- Nearest Neighbor, Naïve Bayesian Classifier, Decision tree Algorithm (C4.5), Genetic Algorithm, Logistic Regression, Artificial Neural network, K-means clustering, EM algorithm, Fuzzy Logic and Hidden Markov Chain; along with addressing the advantages and disadvantages of each of them.

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Index Terms

Computer Science
Information Sciences

Keywords

Intrusion Detection Data mining Neural Networks Fuzzy Logic Support Vector Machine Network Security Naïve Bayes classifier Genetic Algorithm K-Nearest Neighbor Logistic Regression K-means clustering The EM algorithm Decision trees C4.5 Hidden Markov Chain.

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