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Features selection for ids in encrypted traffic using genetic algorithm

Intrusion Detection System (IDS) is one method to detect unauthorized intrusions into computer systems and networks. On the other hand, encrypted exchanges between users are widely used to ensure data security. Traditional IDSs are not able to reactive efficiently in encrypted and tunneled traffic d...

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Main Authors: Barati, Mehdi, Abdullah, Azizol, Mahmod, Ramlan, Mustapha, Norwati, Udzir, Nur Izura
Format: Conference or Workshop Item
Language:English
Published: UUM College of Arts and Sciences, Universiti Utara Malaysia 2013
Online Access:http://psasir.upm.edu.my/id/eprint/41307/1/41307.pdf
http://psasir.upm.edu.my/id/eprint/41307/
http://www.icoci.cms.net.my/proceedings/2013/PDF/PID38.pdf
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id my.upm.eprints.41307
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spelling my.upm.eprints.413072015-11-03T03:26:30Z http://psasir.upm.edu.my/id/eprint/41307/ Features selection for ids in encrypted traffic using genetic algorithm Barati, Mehdi Abdullah, Azizol Mahmod, Ramlan Mustapha, Norwati Udzir, Nur Izura Intrusion Detection System (IDS) is one method to detect unauthorized intrusions into computer systems and networks. On the other hand, encrypted exchanges between users are widely used to ensure data security. Traditional IDSs are not able to reactive efficiently in encrypted and tunneled traffic due to inability to analyze packet content. An encrypted malicious traffic is able to evade the detection by IDS. Feature selection for IDS is a fundamental step in detection procedure and aims to eliminate some irrelevant and unneeded features from the dataset. This paper presents a hybrid feature selection using Genetic Algorithm and Bayesian Network to improve Brute Force attack detection in Secure Shell (SSH) traffic. Brute Force attack traffic collected in a client-server model is implemented in proposed method. Our results prove that the most efficient features were selected by proposed method. UUM College of Arts and Sciences, Universiti Utara Malaysia 2013 Conference or Workshop Item NonPeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/41307/1/41307.pdf Barati, Mehdi and Abdullah, Azizol and Mahmod, Ramlan and Mustapha, Norwati and Udzir, Nur Izura (2013) Features selection for ids in encrypted traffic using genetic algorithm. In: 4th International Conference on Computing and Informatics (ICOCI 2013), 28-30 Aug. 2013, Sarawak, Malaysia. (pp. 279-285). http://www.icoci.cms.net.my/proceedings/2013/PDF/PID38.pdf
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description Intrusion Detection System (IDS) is one method to detect unauthorized intrusions into computer systems and networks. On the other hand, encrypted exchanges between users are widely used to ensure data security. Traditional IDSs are not able to reactive efficiently in encrypted and tunneled traffic due to inability to analyze packet content. An encrypted malicious traffic is able to evade the detection by IDS. Feature selection for IDS is a fundamental step in detection procedure and aims to eliminate some irrelevant and unneeded features from the dataset. This paper presents a hybrid feature selection using Genetic Algorithm and Bayesian Network to improve Brute Force attack detection in Secure Shell (SSH) traffic. Brute Force attack traffic collected in a client-server model is implemented in proposed method. Our results prove that the most efficient features were selected by proposed method.
format Conference or Workshop Item
author Barati, Mehdi
Abdullah, Azizol
Mahmod, Ramlan
Mustapha, Norwati
Udzir, Nur Izura
spellingShingle Barati, Mehdi
Abdullah, Azizol
Mahmod, Ramlan
Mustapha, Norwati
Udzir, Nur Izura
Features selection for ids in encrypted traffic using genetic algorithm
author_facet Barati, Mehdi
Abdullah, Azizol
Mahmod, Ramlan
Mustapha, Norwati
Udzir, Nur Izura
author_sort Barati, Mehdi
title Features selection for ids in encrypted traffic using genetic algorithm
title_short Features selection for ids in encrypted traffic using genetic algorithm
title_full Features selection for ids in encrypted traffic using genetic algorithm
title_fullStr Features selection for ids in encrypted traffic using genetic algorithm
title_full_unstemmed Features selection for ids in encrypted traffic using genetic algorithm
title_sort features selection for ids in encrypted traffic using genetic algorithm
publisher UUM College of Arts and Sciences, Universiti Utara Malaysia
publishDate 2013
url http://psasir.upm.edu.my/id/eprint/41307/1/41307.pdf
http://psasir.upm.edu.my/id/eprint/41307/
http://www.icoci.cms.net.my/proceedings/2013/PDF/PID38.pdf
_version_ 1643832960080674816
score 13.211869

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