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    Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10259/9761

    Título
    A Comparison of AI-Enabled Techniques for the Detection of Attacks in IoT Devices
    Autor
    Cabeza-Lopez, Eduardo Manuel
    Ruiz González, RubénAutoridad UBU Orcid
    Merino Gómez, AlejandroAutoridad UBU Orcid
    Curiel Herrera, Leticia ElenaAutoridad UBU Orcid
    Rincón Arango, Jaime AndrésAutoridad UBU Orcid
    Publicado en
    International Joint Conferences (CISIS 2024, ICEUTE 2024), p. 227-236
    Editorial
    Springer
    Fecha de publicación
    2024-11
    ISBN
    978-3-031-75016-8
    DOI
    10.1007/978-3-031-75016-8_21
    Descripción
    Comunicación presentada en: 17th International Conference on Computational Intelligence in Security for Information Systems (CISIS 2024) and 15th International Conference on European Transnational Education (ICEUTE 2024), 9-11 October 2024, Salamanca (Spain)
    Abstract
    The purpose of this research is to enhance the security of Internet of Things devices. The deployment of these gadgets has increased exponentially, nowadays they can be found in every sector from industrial environments to applications in residential homes. This technology is important for enterprises due to the ability to manage different kind of data and control critical operations in an efficiently way. Consequently, these devices have become frequent targets of cyberattacks, highlighting the necessity of robust detection methods. However it is a complicated task to find a uniform security solution due to the variety of devices, with different capabilities and requirements. This study evaluates some artificial intelligence supervised learning algorithms such as XGBoost and Random Forest using the TON_IoT dataset, which are new generations of Internet of Things and Industrial datasets for evaluating the fidelity and the efficiency of different cybersecurity applications based on Artificial Intelligence. The analysis shows both algorithms are effective in detecting cyberattacks, achieving accuracies close to 98%, with a minimal variation in terms of precision and recall. These algorithms outperform the results obtained in previous works with the same dataset, then they can provide an additional security layer, allowing accurate identification of potential attacks. This research shows the importance of artificial intelligence algorithms in cybersecurity and their potential to improve the protection of this kind of devices.
    Palabras clave
    Artificial Intelligence (AI)
    Internet of Things (IoT)
    XGBoost
    Random Forests
    Attack detection
    TON_IoT dataset
    Materia
    Informática
    Computer science
    URI
    http://hdl.handle.net/10259/9761
    Versión del editor
    https://doi.org/10.1007/978-3-031-75016-8_21
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