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.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

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


    Beteiligte:
    Kannan, A. (Autor:in) / Maguire, G.Q. (Autor:in) / Sharma, A. (Autor:in) / Schoo, P. (Autor:in)

    Erscheinungsdatum :

    2012-01-01


    Anmerkungen:

    Fraunhofer AISEC



    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Klassifikation :

    DDC:    006 / 629



    Genetic algorithm based feature selection for target detection in SAR images

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


    Feature Selection Based on Genetic Algorithm for CBIR

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


    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