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.


    Access

    Download


    Export, share and cite



    Title :

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


    Contributors:
    Kannan, A. (author) / Maguire, G.Q. (author) / Sharma, A. (author) / Schoo, P. (author)

    Publication date :

    2012-01-01


    Remarks:

    Fraunhofer AISEC



    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    629 / 006



    Feature Selection Based on Genetic Algorithm for CBIR

    Zhao, Tianzhong / Lu, Jianjiang / Zhang, Yafei et al. | IEEE | 2008


    Genetic algorithm based feature selection for target detection in SAR images

    Bhanu, B. / Lin, Y. | British Library Online Contents | 2003


    Signature-Anomaly Based Intrusion Detection Algorithm

    Kumar, Roshan / Sharma, Deepak | IEEE | 2018


    Genetic algorithm with variable length chromosomes for network intrusion detection

    Pawar, S. N. / Bichkar, R. S. | British Library Online Contents | 2015