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Reseach Article

Intrusion Detection based on Incremental Combining Classifiers

Published on December 2015 by Dipali Bhosale, Roshani Ade
National Conference on Advances in Computing
Foundation of Computer Science USA
NCAC2015 - Number 3
December 2015
Authors: Dipali Bhosale, Roshani Ade
fd958353-20cc-41b3-bcbe-5f7a756dc5c5

Dipali Bhosale, Roshani Ade . Intrusion Detection based on Incremental Combining Classifiers. National Conference on Advances in Computing. NCAC2015, 3 (December 2015), 34-41.

@article{
author = { Dipali Bhosale, Roshani Ade },
title = { Intrusion Detection based on Incremental Combining Classifiers },
journal = { National Conference on Advances in Computing },
issue_date = { December 2015 },
volume = { NCAC2015 },
number = { 3 },
month = { December },
year = { 2015 },
issn = 0975-8887,
pages = { 34-41 },
numpages = 8,
url = { /proceedings/ncac2015/number3/23740-5021/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 National Conference on Advances in Computing
%A Dipali Bhosale
%A Roshani Ade
%T Intrusion Detection based on Incremental Combining Classifiers
%J National Conference on Advances in Computing
%@ 0975-8887
%V NCAC2015
%N 3
%P 34-41
%D 2015
%I International Journal of Computer Applications
Abstract

Intrusion detection (ID) is the task of analysis the event occurring on a network system in order to detect abnormal activity. Intrusion Detection System has increased due to its more constructive working than traditional security mechanisms. As the network data is dynamic in nature, it leads to the problem of incremental learning of dynamic data. Now, combining classifiers is a new method for the improving classifiers robustness and accuracy. Most of ensemble methods operates in batch mode. For this purpose, proposed system incremental combining classifiers that combines three classifiers that operates incrementally on dynamic data, Naïve Bayes, K-star, Non Nested Generalised Exemplars classifiers based on voting approach. In incremental learning process, numbers of hypotheses are generated during classification; an ensemble decision method is required to aggregate all the votes from multiple hypotheses for the final decision process which produces better accuracy in most of the cases in experiments.

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Index Terms

Computer Science
Information Sciences

Keywords

Security Algorithms