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Reseach Article

Intrusion Detection System with Multi Layer using Bayesian Networks

by Jasreena Kaur Bains, Kiran Kumar Kaki, Kapil Sharma
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 67 - Number 5
Year of Publication: 2013
Authors: Jasreena Kaur Bains, Kiran Kumar Kaki, Kapil Sharma
10.5120/11388-6680

Jasreena Kaur Bains, Kiran Kumar Kaki, Kapil Sharma . Intrusion Detection System with Multi Layer using Bayesian Networks. International Journal of Computer Applications. 67, 5 ( April 2013), 1-4. DOI=10.5120/11388-6680

@article{ 10.5120/11388-6680,
author = { Jasreena Kaur Bains, Kiran Kumar Kaki, Kapil Sharma },
title = { Intrusion Detection System with Multi Layer using Bayesian Networks },
journal = { International Journal of Computer Applications },
issue_date = { April 2013 },
volume = { 67 },
number = { 5 },
month = { April },
year = { 2013 },
issn = { 0975-8887 },
pages = { 1-4 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume67/number5/11388-6680/ },
doi = { 10.5120/11388-6680 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:23:50.072831+05:30
%A Jasreena Kaur Bains
%A Kiran Kumar Kaki
%A Kapil Sharma
%T Intrusion Detection System with Multi Layer using Bayesian Networks
%J International Journal of Computer Applications
%@ 0975-8887
%V 67
%N 5
%P 1-4
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In the era of network security, intrusion detection system plays a vital to detect real – time intrusions, and to execute work to stop the attack. Being everything shifting to internet, security became the foremost preference. In real world, the minority attacks R2L (Remote-To-User) and U2R (User-To-Root) are more hazardous than Probe and DoS (Denial-Of-Service) majority attacks. Present IDS are not much efficient to detect these low level attacks. Therefore, it is extremely important to improve the detection performance for the R2L and U2R attacks with the majority attacks. In this paper hierarchical layered approach for improving detection rate of minority attacks as well as majority attacks is propound. The propound model used Naive bayes classifier with K2 learning process on reduced NSL KDD dataset for each attack class. In this method every layer is individually trained to detect a single type of attack category and the outcome of one layer is passed into another layer to increase the detection rate and for better categorization of both the majority and minority attacks.

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Index Terms

Computer Science
Information Sciences

Keywords

Intrusion detection system (IDS) Network security Feature selection naive bayes classifier R2U U2R DoS