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Adaptive Layered Approach using C5.0 Decision Tree for Intrusion Detection Systems (ALIDS)

by Sherif M. Badr
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 66 - Number 22
Year of Publication: 2013
Authors: Sherif M. Badr
10.5120/11247-5956

Sherif M. Badr . Adaptive Layered Approach using C5.0 Decision Tree for Intrusion Detection Systems (ALIDS). International Journal of Computer Applications. 66, 22 ( March 2013), 18-22. DOI=10.5120/11247-5956

@article{ 10.5120/11247-5956,
author = { Sherif M. Badr },
title = { Adaptive Layered Approach using C5.0 Decision Tree for Intrusion Detection Systems (ALIDS) },
journal = { International Journal of Computer Applications },
issue_date = { March 2013 },
volume = { 66 },
number = { 22 },
month = { March },
year = { 2013 },
issn = { 0975-8887 },
pages = { 18-22 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume66/number22/11247-5956/ },
doi = { 10.5120/11247-5956 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:23:06.559471+05:30
%A Sherif M. Badr
%T Adaptive Layered Approach using C5.0 Decision Tree for Intrusion Detection Systems (ALIDS)
%J International Journal of Computer Applications
%@ 0975-8887
%V 66
%N 22
%P 18-22
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Intrusion Detection System (IDS) is one of a crucial issue and a major research problem in network security. This work, An Adaptive multi-Layer Intrusion Detection System (ALIDS) is designed and developed to achieve high efficiency, scalability, flexibility and improve the detection and classification rate accuracy. We apply C5 decision tree on our model. Our experimental results showed that the proposed ALIDS model with different order of training classes enhances the accuracy of U2R and R2L.

References
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Index Terms

Computer Science
Information Sciences

Keywords

component network intrusion detection Decision Tree

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