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Adaptive Intrusion Detection based on Boosting and NaÔve Bayesian Classifier

by Dewan Md. Farid, Mohammad Zahidur Rahman, Chowdhury Mofizur Rahman
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 24 - Number 3
Year of Publication: 2011
Authors: Dewan Md. Farid, Mohammad Zahidur Rahman, Chowdhury Mofizur Rahman
10.5120/2932-3883

Dewan Md. Farid, Mohammad Zahidur Rahman, Chowdhury Mofizur Rahman . Adaptive Intrusion Detection based on Boosting and NaÔve Bayesian Classifier. International Journal of Computer Applications. 24, 3 ( June 2011), 12-19. DOI=10.5120/2932-3883

@article{ 10.5120/2932-3883,
author = { Dewan Md. Farid, Mohammad Zahidur Rahman, Chowdhury Mofizur Rahman },
title = { Adaptive Intrusion Detection based on Boosting and NaÔve Bayesian Classifier },
journal = { International Journal of Computer Applications },
issue_date = { June 2011 },
volume = { 24 },
number = { 3 },
month = { June },
year = { 2011 },
issn = { 0975-8887 },
pages = { 12-19 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume24/number3/2932-3883/ },
doi = { 10.5120/2932-3883 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:10:01.147545+05:30
%A Dewan Md. Farid
%A Mohammad Zahidur Rahman
%A Chowdhury Mofizur Rahman
%T Adaptive Intrusion Detection based on Boosting and NaÔve Bayesian Classifier
%J International Journal of Computer Applications
%@ 0975-8887
%V 24
%N 3
%P 12-19
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, we introduce a new learning algorithm for adaptive intrusion detection using boosting and naïve Bayesian classifier, which considers a series of classifiers and combines the votes of each individual classifier for classifying an unknown or known example. The proposed algorithm generates the probability set for each round using naïve Bayesian classifier and updates the weights of training examples based on the misclassification error rate that produced by the training examples in each round. This algorithm addresses the problem of classifying the large intrusion detection dataset, which improves the detection rates (DR) and reduces the false positives (FP) at acceptable level in intrusion detection. We tested the performance of the proposed algorithm with existing data mining algorithms by employing on the KDD99 benchmark intrusion detection dataset, and the experimental results proved that the proposed algorithm achieved high detection rates and significantly reduced the number of false positives for different types of network intrusions.

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Index Terms

Computer Science
Information Sciences

Keywords

Boosting Naïve Bayesian Classifier Intrusion Detection Detection Rate False Positive

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