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Genetic algorithm based feature selection algorithm for effective intrusion detection in cloud networks

 
: Kannan, A.; Maguire, G.Q.; Sharma, A.; Schoo, P.

:
Preprint urn:nbn:de:0011-n-2414751 (304 KByte PDF)
MD5 Fingerprint: 9e61ba4744551b8d0505a9f4c474e9b3
© 2012 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Created on: 18.5.2013


Vreeken, J. (Hrsg.) ; Institute of Electrical and Electronics Engineers -IEEE-; IEEE Computer Society:
IEEE 12th International Conference on Data Mining Workshops, ICDMW 2012. Proceedings. Pt.1 : Brussels, Belgium, 10 December 2012
Piscataway, NJ: IEEE, 2012
ISBN: 978-1-4673-5164-5
ISBN: 978-0-7695-4925-5
pp.416-423
International Conference on Data Mining Workshops (ICDMW) <12, 2012, Brussels>
International Workshop on Knowledge Discovery Using Cloud and Distributed Computing Platforms (KDCloud) <3, 2012, Brussels>
European Commission EC
FP7-ICT; 257448; SAIL
English
Conference Paper, Electronic Publication
Fraunhofer AISEC ()
intrusion detection system; genetic algorithm; Fuzzy Support Vector Machine (FSVM); tenfold cross validation

Abstract
Cloud computing is expected to provide on-demand, agile, and elastic services. Cloud networking extends cloud computing by providing virtualized networking functionalities and allows various optimizations, for example to reduce latency while increasing flexibility in the placement, movement, and interconnection of these virtual resources. However, this approach introduces new security challenges. In this paper, we propose a new intrusion detection model in which we combine a newly proposed genetic based feature selection algorithm and an existing Fuzzy Support Vector Machines (SVM) for effective classification as a solution. The feature selection reduces the number of features by removing unimportant features, hence reducing runtime. Moreover, when the Fuzzy SVM classifier is used with the reduced feature set, it improves the detection accuracy. Experimental results of the proposed combination of feature selection and classification model detects anomalies with a low false alarm rate and a high detection rate when tested with the KDD Cup 99 data set.

: http://publica.fraunhofer.de/documents/N-241475.html