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Adaptive Layered Approach using Machine Learning Techniques with Gain Ratio for Intrusion Detection Systems

by Heba Ezzat Ibrahim, Sherif M. Badr, Mohamed A. Shaheen
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 56 - Number 7
Year of Publication: 2012
Authors: Heba Ezzat Ibrahim, Sherif M. Badr, Mohamed A. Shaheen
10.5120/8901-2928

Heba Ezzat Ibrahim, Sherif M. Badr, Mohamed A. Shaheen . Adaptive Layered Approach using Machine Learning Techniques with Gain Ratio for Intrusion Detection Systems. International Journal of Computer Applications. 56, 7 ( October 2012), 10-16. DOI=10.5120/8901-2928

@article{ 10.5120/8901-2928,
author = { Heba Ezzat Ibrahim, Sherif M. Badr, Mohamed A. Shaheen },
title = { Adaptive Layered Approach using Machine Learning Techniques with Gain Ratio for Intrusion Detection Systems },
journal = { International Journal of Computer Applications },
issue_date = { October 2012 },
volume = { 56 },
number = { 7 },
month = { October },
year = { 2012 },
issn = { 0975-8887 },
pages = { 10-16 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume56/number7/8901-2928/ },
doi = { 10.5120/8901-2928 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:59:16.056022+05:30
%A Heba Ezzat Ibrahim
%A Sherif M. Badr
%A Mohamed A. Shaheen
%T Adaptive Layered Approach using Machine Learning Techniques with Gain Ratio for Intrusion Detection Systems
%J International Journal of Computer Applications
%@ 0975-8887
%V 56
%N 7
%P 10-16
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Intrusion Detection System (IDS) has increasingly become a crucial issue for computer and network systems. Optimizing performance of IDS becomes an important open problem which receives more and more attention from the research community. In this work, A multi-layer intrusion detection model is designed and developed to achieve high efficiency and improve the detection and classification rate accuracy . we effectively apply Machine learning techniques (C5 decision tree, Multilayer Perceptron neural network and Naïve Bayes) using gain ratio for selecting the best features for each layer as to use smaller storage space and get higher Intrusion detection performance. Our experimental results showed that the proposed multi-layer model using C5 decision tree achieves higher classification rate accuracy, using feature selection by Gain Ratio, and less false alarm rate than MLP and naïve Bayes. Using Gain Ratio enhances the accuracy of U2R and R2L for the three machine learning techniques (C5, MLP and Naïve Bayes) significantly. MLP has high classification rate when using the whole 41 features in Dos and Probe layers.

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Index Terms

Computer Science
Information Sciences

Keywords

Intrusion Detection Layered Approach Machine Learning NSL-KDD dataset Network Security

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