Classification of habitual activities in behavior-based network detection

This paper presents a new method to detect network traffic threats based on packet classification which is result from the identification of insider’s habitual activities. We assess the habitual activities by examining regular expression of web applications use by insiders together with the existing...

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Main Authors: Stiawan, Deris, Abdullah, Abdul Hanan, Idris, Mohd. Yazid
Format: Article
Language:English
Published: Journal of Computing 2010
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Online Access:http://eprints.utm.my/id/eprint/37256/2/index.html
http://eprints.utm.my/id/eprint/37256/
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spelling my.utm.372562017-10-13T11:04:00Z http://eprints.utm.my/id/eprint/37256/ Classification of habitual activities in behavior-based network detection Stiawan, Deris Abdullah, Abdul Hanan Idris, Mohd. Yazid QA75 Electronic computers. Computer science This paper presents a new method to detect network traffic threats based on packet classification which is result from the identification of insider’s habitual activities. We assess the habitual activities by examining regular expression of web applications use by insiders together with the existing server activities log and rules pattern from global update. We capture the packets, analyze the packet and finally, categorize into three main categories whether it is normal, suspicious or malicious. Our method is able to detect threat with low false alarm rate and provides event list handler to rate the risk for prevention purposes. We apply our method to evaluate system security for help security officer (IT Manager and Administrator) to be aware of status network activities. Journal of Computing 2010-08 Article PeerReviewed text/html en http://eprints.utm.my/id/eprint/37256/2/index.html Stiawan, Deris and Abdullah, Abdul Hanan and Idris, Mohd. Yazid (2010) Classification of habitual activities in behavior-based network detection. Journal of Computing, 2 (8). pp. 1-7. ISSN 2151-9617
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Stiawan, Deris
Abdullah, Abdul Hanan
Idris, Mohd. Yazid
Classification of habitual activities in behavior-based network detection
description This paper presents a new method to detect network traffic threats based on packet classification which is result from the identification of insider’s habitual activities. We assess the habitual activities by examining regular expression of web applications use by insiders together with the existing server activities log and rules pattern from global update. We capture the packets, analyze the packet and finally, categorize into three main categories whether it is normal, suspicious or malicious. Our method is able to detect threat with low false alarm rate and provides event list handler to rate the risk for prevention purposes. We apply our method to evaluate system security for help security officer (IT Manager and Administrator) to be aware of status network activities.
format Article
author Stiawan, Deris
Abdullah, Abdul Hanan
Idris, Mohd. Yazid
author_facet Stiawan, Deris
Abdullah, Abdul Hanan
Idris, Mohd. Yazid
author_sort Stiawan, Deris
title Classification of habitual activities in behavior-based network detection
title_short Classification of habitual activities in behavior-based network detection
title_full Classification of habitual activities in behavior-based network detection
title_fullStr Classification of habitual activities in behavior-based network detection
title_full_unstemmed Classification of habitual activities in behavior-based network detection
title_sort classification of habitual activities in behavior-based network detection
publisher Journal of Computing
publishDate 2010
url http://eprints.utm.my/id/eprint/37256/2/index.html
http://eprints.utm.my/id/eprint/37256/
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score 13.160551