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    Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10259/7240

    Título
    Towards a Reliable Comparison and Evaluation of Network Intrusion Detection Systems Based on Machine Learning Approaches
    Autor
    Magán-Carrion, Roberto
    Urda Muñoz, DanielAutoridad UBU Orcid
    Díaz-Cano, Ignacio
    Dorronsoro, Bernabé
    Publicado en
    Applied sciences. 2020, V. 10, n. 5, e1775
    Editorial
    MDPI
    Fecha de publicación
    2020-03
    DOI
    10.3390/app10051775
    Abstract
    Presently, we are living in a hyper-connected world where millions of heterogeneous devices are continuously sharing information in different application contexts for wellness, improving communications, digital businesses, etc. However, the bigger the number of devices and connections are, the higher the risk of security threats in this scenario. To counteract against malicious behaviours and preserve essential security services, Network Intrusion Detection Systems (NIDSs) are the most widely used defence line in communications networks. Nevertheless, there is no standard methodology to evaluate and fairly compare NIDSs. Most of the proposals elude mentioning crucial steps regarding NIDSs validation that make their comparison hard or even impossible. This work firstly includes a comprehensive study of recent NIDSs based on machine learning approaches, concluding that almost all of them do not accomplish with what authors of this paper consider mandatory steps for a reliable comparison and evaluation of NIDSs. Secondly, a structured methodology is proposed and assessed on the UGR’16 dataset to test its suitability for addressing network attack detection problems. The guideline and steps recommended will definitively help the research community to fairly assess NIDSs, although the definitive framework is not a trivial task and, therefore, some extra effort should still be made to improve its understandability and usability further.
    Palabras clave
    Network intrusion detection
    NIDS
    Machine learning
    Attack detection
    Communications networks
    Methodology
    Materia
    Informática
    Computer science
    URI
    http://hdl.handle.net/10259/7240
    Versión del editor
    https://doi.org/10.3390/app10051775
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