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2012 IEEE 12th International Conference on Data Mining Workshops (2012)
Brussels, Belgium Belgium
Dec. 10, 2012 to Dec. 10, 2012
ISBN: 978-1-4673-5164-5
pp: 416-423
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.
Classification algorithms, Intrusion detection, Feature extraction, Computer architecture, Support vector machines, Genetics, tenfold cross validation, Intrusion Detection System (IDS), Genetic Algorithm (GA), Fuzzy Support Vector Machine (FSVM)

A. Kannan, G. Q. Maguire Jr., A. Sharma and P. Schoo, "Genetic Algorithm Based Feature Selection Algorithm for Effective Intrusion Detection in Cloud Networks," 2012 IEEE 12th International Conference on Data Mining Workshops(ICDMW), Brussels, Belgium Belgium, 2012, pp. 416-423.
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