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    Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10259/3863

    Título
    Neural visualization of network traffic data for intrusion detection
    Autor
    Corchado, EmilioAutoridad UBU Orcid
    Herrero Cosío, ÁlvaroAutoridad UBU Orcid
    Publicado en
    Applied soft computing. 2011, V. 11, n. 2, p. 2042-2056
    Editorial
    Elsevier
    Fecha de publicación
    2011-03
    ISSN
    1568-4946
    DOI
    10.1016/j.asoc.2010.07.002
    Abstract
    This study introduces and describes a novel intrusion detection system (IDS) called MOVCIDS (mobile visualization connectionist IDS). This system applies neural projection architectures to detect anomalous situations taking place in a computer network. By its advanced visualization facilities, the proposed IDS allows providing an overview of the network traffic as well as identifying anomalous situations tackled by computer networks, responding to the challenges presented by volume, dynamics and diversity of the traffic, including novel (0-day) attacks. MOVCIDS provides a novel point of view in the field of IDSs by enabling the most interesting projections (based on the fourth order statistics; the kurtosis index) of a massive traffic dataset to be extracted. These projections are then depicted through a functional and mobile visualization interface, providing visual information of the internal structure of the traffic data. The interface makes MOVCIDS accessible from any mobile device to give more accessibility to network administrators, enabling continuous visualization, monitoring and supervision of computer networks. Additionally, a novel testing technique has been developed to evaluate MOVCIDS and other IDSs employing numerical datasets. To show the performance and validate the proposed IDS, it has been tested in different real domains containing several attacks and anomalous situations. In addition, the importance of the temporal dimension on intrusion detection, and the ability of this IDS to process it, are emphasized in this work
    Palabras clave
    Neural and exploratory projection techniques
    Connectionist unsupervised models
    Computer network security
    Intrusion detection
    Network traffic visualization
    Materia
    Computer science
    Informática
    URI
    http://hdl.handle.net/10259/3863
    Versión del editor
    http://dx.doi.org/10.1016/j.asoc.2010.07.002
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