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Improving the Intrusion Detection using Discriminative Machine Learning Approach and Improve the Time Complexity by Data Mining Feature Selection Methods

by Karan Bajaj, Amit Arora
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 76 - Number 1
Year of Publication: 2013
Authors: Karan Bajaj, Amit Arora
10.5120/13209-0587

Karan Bajaj, Amit Arora . Improving the Intrusion Detection using Discriminative Machine Learning Approach and Improve the Time Complexity by Data Mining Feature Selection Methods. International Journal of Computer Applications. 76, 1 ( August 2013), 5-11. DOI=10.5120/13209-0587

@article{ 10.5120/13209-0587,
author = { Karan Bajaj, Amit Arora },
title = { Improving the Intrusion Detection using Discriminative Machine Learning Approach and Improve the Time Complexity by Data Mining Feature Selection Methods },
journal = { International Journal of Computer Applications },
issue_date = { August 2013 },
volume = { 76 },
number = { 1 },
month = { August },
year = { 2013 },
issn = { 0975-8887 },
pages = { 5-11 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume76/number1/13209-0587/ },
doi = { 10.5120/13209-0587 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:44:45.845177+05:30
%A Karan Bajaj
%A Amit Arora
%T Improving the Intrusion Detection using Discriminative Machine Learning Approach and Improve the Time Complexity by Data Mining Feature Selection Methods
%J International Journal of Computer Applications
%@ 0975-8887
%V 76
%N 1
%P 5-11
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

As the dependence of daily life is increasing on Internet technology, the attacks on the systems, servers are also rapidly increasing. The motives of attacks are to steal the confidential data from the systems or making the system unavailable to the authorised users. An effective approach is required to detect the intrusions to provide the defence to the Networks. First we applied the feature selection to reduce the dimensions of NSL-KDD data set. By feature reduction and machine learning approach we able to build Intrusion detection model to find attacks on system and improve the intrusion detection using the captured data. The intrusion detection accuracy of learning algorithms is also performed on the data set, without the level 21 attacks which is most easy to identify attacks, using learning algorithms and the success rate of proposed model is calculated over the attacks which are hard to detect.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Feature Selection Weka NSL-KDD data set Accuracy Intrusion detection Machine learning

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