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General Information
    • ISSN: 2301-3559
    • Frequency: Quarterly
    • DOI: 10.18178/LNSE
    • Editor-in-Chief: Prof. Jemal Antidze
    • Executive Editor: Ms. Nina Lee
    • Abstracting/ Indexing: EI (INSPEC, IET), DOAJ, Electronic Journals Library, Engineering & Technology Digital Library, Ulrich's Periodicals Directory, International Computer Science Digital Library (ICSDL), ProQuest and Google Scholar.
    • E-mail: lnse@ejournal.net
Editor-in-chief
Prof. Jemal Antidze
I. Vekua Scientific Institute of Applied Mathematics
Tbilisi State University, Georgia
I'm happy to take on the position of editor in chief of LNSE. We encourage authors to submit papers concerning any branch of Software Engineering.

LNSE 2013 Vol.1(3): 273-278 ISSN: 2301-3559
DOI: 10.7763/LNSE.2013.V1.60

An Extended Modified Fuzzy Possibilistic C-Means Clustering Algorithm for Intrusion Detection

Sarab M. Hameed, Sumaya Saad, and Mayyadah F. AlAni
Abstract—Computer intruders have become a major threat due to their wide spread through the Internet. Therefore, there was a need for security technique that monitors computer resources and sends reports on the activities of any anomaly or strange patterns which called Intrusion Detection (ID). A fuzzy clustering algorithm used because the boundaries between normal and intrusive cannot be well distinct due to the uncertainty nature of an attack. This paper proposed an algorithm for ID that combines both Modified fuzzy possiblistic C-Means (MFPCM) and symbolic fuzzy clustering in one algorithm called Extended Modified Fuzzy Possiblistic C-means (EMFPCM). To evaluate the EMFPCM, Knowledge Discovery and Data Mining Cup 1999 (KDD cup 99) intrusion detection dataset was used. The results indicated that the proposed algorithm was able to distinguish between normal and attack behaviors with high detection rate.

Index Terms—Fuzzy clustering, modified fuzzy possibilistic C-mean, intrusion detection, mixed features, symbolic data.

The authors are with the Computer Science Department, University of Baghdad, Jadiriya, P.O.Box 17635, Iraq (e-mail: sarab_majeed@ scbaghdad.edu.iq, sumasaad@yahoo.com, mfaisal@uob.edu.bh).

[PDF]

Cite: Sarab M. Hameed, Sumaya Saad, and Mayyadah F. AlAni, "An Extended Modified Fuzzy Possibilistic C-Means Clustering Algorithm for Intrusion Detection," Lecture Notes on Software Engineering vol. 1, no. 3, pp. 273-278, 2013.

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