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    Por favor, use este identificador para citar o enlazar este ítem:https://uvadoc.uva.es/handle/10324/54271

    Título
    Application of deep reinforcement learning to intrusion detection for supervised problems
    Autor
    López Martín, ManuelAutoridad UVA
    Carro Martínez, BelénAutoridad UVA Orcid
    Sánchez Esguevillas, Antonio JavierAutoridad UVA Orcid
    Año del Documento
    2020
    Editorial
    Elsevier
    Descripción
    Producción Científica
    Documento Fuente
    Expert Systems with Applications Volume 141, 2020, 112963
    Resumen
    The application of new techniques to increase the performance of intrusion detection systems is crucial in modern data networks with a growing threat of cyber-attacks. These attacks impose a greater risk on network services that are increasingly important from a social end economical point of view. In this work we present a novel application of several deep reinforcement learning (DRL) algorithms to intrusion detection using a labeled dataset. We present how to perform supervised learning based on a DRL framework. The implementation of a reward function aligned with the detection of intrusions is extremely difficult for Intrusion Detection Systems (IDS) since there is no automatic way to identify intrusions. Usually the identification is performed manually and stored in datasets of network features associated with intrusion events. These datasets are used to train supervised machine learning algorithms for classifying intrusion events. In this paper we apply DRL using two of these datasets: NSL-KDD and AWID datasets. As a novel approach, we have made a conceptual modification of the classic DRL paradigm (based on interaction with a live environment), replacing the environment with a sampling function of recorded training intrusions. This new pseudo-environment, in addition to sampling the training dataset, generates rewards based on detection errors found during training. We present the results of applying our technique to four of the most relevant DRL models: Deep Q-Network (DQN), Double Deep Q-Network (DDQN), Policy Gradient (PG) and Actor-Critic (AC). The best results are obtained for the DDQN algorithm. We show that DRL, with our model and some parameter adjustments, can improve the results of intrusion detection in comparison with current machine learning techniques. Besides, the classifier obtained with DRL is faster than alternative models. A comprehensive comparison of the results obtained with other machine learning models is provided for the AWID and NSL-KDD datasets, together with the lessons learned from the application of several design alternatives to the four DRL models.
    Materias (normalizadas)
    Seguridad informática
    Materias Unesco
    3325 Tecnología de las Telecomunicaciones
    Palabras Clave
    Data networks
    Redes de datos
    Intrusion detection
    Detección de intrusos
    Deep reinforcement learning (DRL)
    Aprendizaje de refuerzo profundo (DRL)
    ISSN
    0957-4174
    Revisión por pares
    SI
    DOI
    10.1016/j.eswa.2019.112963
    Version del Editor
    https://www.sciencedirect.com/science/article/pii/S0957417419306815?via%3Dihub
    Propietario de los Derechos
    © 2020 The Author(s)
    Idioma
    eng
    URI
    https://uvadoc.uva.es/handle/10324/54271
    Tipo de versión
    info:eu-repo/semantics/submittedVersion
    Derechos
    openAccess
    Aparece en las colecciones
    • DEP71 - Artículos de revista [358]
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    Attribution-NonCommercial-NoDerivatives 4.0 InternacionalLa licencia del ítem se describe como Attribution-NonCommercial-NoDerivatives 4.0 Internacional

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