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Reseach Article

An Effective Intrusion System for Mobile Ad Hoc Networks using Rough Set Theory and Support Vector Machine

Published on December 2012 by P. Sivaranjanadevi, M. Geetanjali, S. Balaganesh, T. Poongothai
EGovernance and Cloud Computing Services - 2012
Foundation of Computer Science USA
EGOV - Number 2
December 2012
Authors: P. Sivaranjanadevi, M. Geetanjali, S. Balaganesh, T. Poongothai
497f953c-8637-457e-bc29-f7bcd50d4f1d

P. Sivaranjanadevi, M. Geetanjali, S. Balaganesh, T. Poongothai . An Effective Intrusion System for Mobile Ad Hoc Networks using Rough Set Theory and Support Vector Machine. EGovernance and Cloud Computing Services - 2012. EGOV, 2 (December 2012), 1-7.

@article{
author = { P. Sivaranjanadevi, M. Geetanjali, S. Balaganesh, T. Poongothai },
title = { An Effective Intrusion System for Mobile Ad Hoc Networks using Rough Set Theory and Support Vector Machine },
journal = { EGovernance and Cloud Computing Services - 2012 },
issue_date = { December 2012 },
volume = { EGOV },
number = { 2 },
month = { December },
year = { 2012 },
issn = 0975-8887,
pages = { 1-7 },
numpages = 7,
url = { /proceedings/egov/number2/9488-1011/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 EGovernance and Cloud Computing Services - 2012
%A P. Sivaranjanadevi
%A M. Geetanjali
%A S. Balaganesh
%A T. Poongothai
%T An Effective Intrusion System for Mobile Ad Hoc Networks using Rough Set Theory and Support Vector Machine
%J EGovernance and Cloud Computing Services - 2012
%@ 0975-8887
%V EGOV
%N 2
%P 1-7
%D 2012
%I International Journal of Computer Applications
Abstract

Mobile Ad Hoc Networks has more challenging vulnerabilities compared with wired networks. Mobile ad hoc networking (MANET) has become an important technology in current years because of the rapid proliferation of wireless devices. They are highly vulnerable to attacks due to the open medium, dynamically changing network topology and lack of centralized monitoring point. It is important to search new architecture and mechanisms to protect these networks. Intrusion detection system (IDS) tools are suitable for securing such networks. The main task of IDS is to discover the intrusion from collected data. Some of the features of collected data may be redundant or contribute little to the detection process. So it is essential to select the important features to increase the detection rate. Most of the existing intrusion detection systems detects the intrusion by using large number of data features collected from network. In this work, we propose anomaly based intrusion detection system to detect the malicious activities by collecting statistics from network. Also we use SVM machine learning technique and Rough Set Theory which are used to detect the attacks in an efficient way. Rough set theory preprocesses the feature data to reduce the computational complexity. The support vector machine is trained by using feature set from the Rough set theory for detecting abnormal behavior.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Support Vector Machine Rough Set Theory Intrusion Detection System Rosetta