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    Título
    Comparison of Network Intrusion Detection Performance Using Feature Representation
    Autor
    Pérez, Daniel
    Alonso, Serafín
    Morán, Antonio
    Prada Medrano, Miguel Angel
    Fuertes Martínez, Juan JoséAutoridad Buleria
    Domínguez, Manuel
    Facultad/Centro
    Escuela de Ingenierias Industrial e Informatica
    Área de conocimiento
    Ingenieria de Sistemas y Automatica
    Título de la revista
    International Conference on Engineering Applications of Neural Networks
    Datos de la obra
    International Conference on Engineering Applications of Neural Networks, 2019
    Editor
    Springer
    Fecha
    2019
    ISSN
    1865-0929
    Abstract
    Intrusion detection is essential for the security of the components of any network. For that reason, several strategies can be used in Intrusion Detection Systems (IDS) to identify the increasing attempts to gain unauthorized access with malicious purposes including those base on machine learning. Anomaly detection has been applied successfully to numerous domains and might help to identify unknown attacks. However, there are existing issues such as high error rates or large dimensionality of data that make its deployment di cult in real-life scenarios. Representation learning allows to estimate new latent features of data in a low-dimensionality space. In this work, anomaly detection is performed using a previous feature learning stage in order to compare these methods for the detection of intrusions in network tra c. For that purpose, four di erent anomaly detection algorithms are applied to recent network datasets using two di erent feature learning methods such as principal component analysis and autoencoders. Several evaluation metrics such as accuracy, F1 score or ROC curves are used for comparing their performance. The experimental results show an improvement for two of the anomaly detection methods using autoencoder and no signi cant variations for the linear feature transformation
    Materia
    Ingeniería de sistemas
    Palabras clave
    Anomaly detection
    Feature representation
    Network intrusion detection
    Peer review
    SI
    URI
    http://hdl.handle.net/10612/11293
    DOI
    10.1007/978-3-030-20257-6_40
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