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    Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10259/3861

    Título
    A neural-visualization IDS for honeynet data
    Autor
    Herrero Cosío, ÁlvaroAutoridad UBU Orcid
    Zurutuza, Urko
    Corchado, EmilioAutoridad UBU Orcid
    Publicado en
    International Journal of Neural Systems. 2012, V. 22, n. 2, 1250005
    Editorial
    World Scientific Publishing
    Fecha de publicación
    2012-04
    ISSN
    0129-0657
    DOI
    10.1142/S0129065712500050
    Abstract
    Neural intelligent systems can provide a visualization of the network traffic for security staff, in order to reduce the widely known high false-positive rate associated with misuse-based Intrusion Detection Systems (IDSs). Unlike previous work, this study proposes an unsupervised neural models that generate an intuitive visualization of the captured traffic, rather than network statistics. These snapshots of network events are immensely useful for security personnel that monitor network behavior. The system is based on the use of different neural projection and unsupervised methods for the visual inspection of honeypot data, and may be seen as a complementary network security tool that sheds light on internal data structures through visual inspection of the traffic itself. Furthermore, it is intended to facilitate verification and assessment of Snort performance (a well-known and widely-used misuse-based IDS), through the visualization of attack patterns. Empirical verification and comparison of the proposed projection methods are performed in a real domain, where two different case studies are defined and analyzed
    Palabras clave
    Artificial Neural Networks
    Unsupervised Learning
    Projection Models
    Network & Computer Security
    Intrusion Detection
    Honeypots
    Materia
    Computer science
    Informática
    URI
    http://hdl.handle.net/10259/3861
    Versión del editor
    http://www.worldscientific.com/doi/abs/10.1142/S0129065712500050
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