research-article

Intelligent intrusion detection system using fuzzy rough set based C4.5 algorithm

Authors:

A. KannanAuthors Info & Claims

ICACCI '12: Proceedings of the International Conference on Advances in Computing, Communications and Informatics

Pages 596 - 601

https://doi.org/10.1145/2345396.2345493

Published: 03 August 2012 Publication History

Abstract

In recent years, as the usage of internet increases, new type of attacks on network information is also increasing continuously. Intrusion Detection System (IDS) is an important component that provides security to the network information by identifying various kinds of attacks occurring in the networks. Currently, there are many researches who are working in this area and they focus on developing effective IDS using machine learning techniques. However, there is a need for better systems with improved detection accuracy and reduced false alarm rate. In this paper, we propose an Intelligent IDS using fuzzy rough set based C4.5 classification algorithm to improve the detection accuracy. This system has been compared with Support Vector Machines for illustrating the improvement with respect to the detection accuracy. The inputs to these classifiers were preprocessed using a fuzzy rough set based outlier detection algorithm. In this work, we used the KDD'99 Cup dataset for carrying out the simulation of the experiments. The experimental results obtained in this work show that the proposed model reduces the false alarm rate and improves overall detection accuracy.

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P. Yogesh, Associate Professor, College of Engineering, Anna University, Chennai. [email protected], 9444366580

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S. Tharunya, Research Scholar, RMK Engineering College, Anna University, Chennai.

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K.L. Shunmuganathan, Professor, RMK Engineering College, Anna University, Chennai. [email protected], 9443125834

Cited By

Ogundokun RMisra SBajeh AOkoro UAhuja R(2022)An Integrated IDS Using ICA-Based Feature Selection and SVM Classification MethodIllumination of Artificial Intelligence in Cybersecurity and Forensics10.1007/978-3-030-93453-8_11(255-271)Online publication date: 1-Jan-2022
https://doi.org/10.1007/978-3-030-93453-8_11
Alsaadi HALmuttari RUcan OBayat O(2021)An adapting soft computing model for intrusion detection systemComputational Intelligence10.1111/coin.1243338:3(855-875)Online publication date: 26-Jan-2021
https://doi.org/10.1111/coin.12433
Nagaraj KBhattacharjee BSridhar AGS S(2018)Detection of phishing websites using a novel twofold ensemble modelJournal of Systems and Information Technology10.1108/JSIT-09-2017-0074Online publication date: 18-Oct-2018
https://doi.org/10.1108/JSIT-09-2017-0074
Show More Cited By

Intelligent intrusion detection system using fuzzy rough set based C4.5 algorithm
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
  2. Machine learning
    1. Learning paradigms
      1. Supervised learning

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Published In

cover image ACM Other conferences

ICACCI '12: Proceedings of the International Conference on Advances in Computing, Communications and Informatics

August 2012

1307 pages

ISBN:9781450311960

DOI:10.1145/2345396

Editors:
Sabu M. Thampi,
El-Sayed El-Afry,
Javier Aguiar,
General Chairs:
K. Gopalan
Purdue University Calumet
,
Sabu M. Thampi
Indian Institute of Information Technology and Management - Kerala, India

Copyright © 2012 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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New York, NY, United States

Publication History

Published: 03 August 2012

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ICACCI '12: 2012 International Conference on Advances in Computing, Communications and Informatics

August 3 - 5, 2012

Chennai, India

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Cited By

Ogundokun RMisra SBajeh AOkoro UAhuja R(2022)An Integrated IDS Using ICA-Based Feature Selection and SVM Classification MethodIllumination of Artificial Intelligence in Cybersecurity and Forensics10.1007/978-3-030-93453-8_11(255-271)Online publication date: 1-Jan-2022
https://doi.org/10.1007/978-3-030-93453-8_11
Alsaadi HALmuttari RUcan OBayat O(2021)An adapting soft computing model for intrusion detection systemComputational Intelligence10.1111/coin.1243338:3(855-875)Online publication date: 26-Jan-2021
https://doi.org/10.1111/coin.12433
Nagaraj KBhattacharjee BSridhar AGS S(2018)Detection of phishing websites using a novel twofold ensemble modelJournal of Systems and Information Technology10.1108/JSIT-09-2017-0074Online publication date: 18-Oct-2018
https://doi.org/10.1108/JSIT-09-2017-0074
Bhuvaneswari GManikandan G(2018)A novel machine learning framework for diagnosing the type 2 diabetics using temporal fuzzy ant miner decision tree classifier with temporal weighted genetic algorithmComputing10.1007/s00607-018-0599-4100:8(759-772)Online publication date: 1-Aug-2018
https://dl.acm.org/doi/10.1007/s00607-018-0599-4

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