2026-05-14T11:03:17Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/97612025-11-16T23:42:18Zcom_10259_8983com_10259_5086com_10259_2604com_10259_3847col_10259_9699col_10259_7109

Cabeza-Lopez, Eduardo Manuel Ruiz González, Rubén Merino Gómez, Alejandro Curiel Herrera, Leticia Elena Rincón Arango, Jaime Andrés 2024-12-05T13:20:24Z 2024-12-05T13:20:24Z 2024-11 978-3-031-75016-8 978-3-031-75015-1 http://hdl.handle.net/10259/9761 10.1007/978-3-031-75016-8_21 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) 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. This publication is part of the AI4SECIoT project (“Artificial Intelligence for Securing IoT Devices”), funded by the National Cybersecurity Institute (INCIBE) - Spanish Ministry of Economic Affairs and Digital Transformation, and the European Union (NextGenerationEU*/PRTR). application/pdf eng Springer International Joint Conferences (CISIS 2024, ICEUTE 2024), p. 227-236 https://doi.org/10.1007/978-3-031-75016-8_21 info:eu-repo/grantAgreement/INCIBE//MR5Ñ05/ES/Inteligencia Artificial para la Securización de Dispositivos IoT/ Artificial Intelligence (AI) Internet of Things (IoT) XGBoost Random Forests Attack detection TON_IoT dataset Informática Computer science A Comparison of AI-Enabled Techniques for the Detection of Attacks in IoT Devices info:eu-repo/semantics/conferenceObject info:eu-repo/semantics/acceptedVersion info:eu-repo/semantics/openAccess 957 227 236