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dc.contributor.authorSevert-Silva, Marcos
dc.contributor.authorCasado Vara, Roberto
dc.contributor.authorMartin del Rey, Ángel
dc.contributor.authorQuintián, Héctor
dc.contributor.authorCalvo-Rolle, José Luis
dc.date.accessioned2024-05-24T13:38:32Z
dc.date.issued2024-05-18
dc.identifier.citationMarcos Severt, Roberto Casado-Vara, Ángel Martín del Rey, Héctor Quintián, Jose Luis Calvo-Rolle, Multi-agent reinforcement learning based algorithm detection of malware-infected nodes in IoT networks, Logic Journal of the IGPL, 2024; jzae068, https://doi.org/10.1093/jigpal/jzae068es_ES
dc.identifier.issn1368-9894
dc.identifier.urihttp://hdl.handle.net/2183/36617
dc.description.abstract[Abstract] The Internet of Things (IoT) is a fast-growing technology that connects everyday devices to the Internet, enabling wireless, low-consumption and low-cost communication and data exchange. IoT has revolutionized the way devices interact with each other and the internet. The more devices become connected, the greater the risk of security breaches. There is currently a need for new approaches to algorithms that can detect malware regardless of the size of the network and that can adapt to dynamic changes in the network. Through the use of a multi-agent reinforcement learning algorithm, this paper proposes a novel algorithm for malware detection in IoT devices. The proposed algorithm is not strongly dependent on the size of the IoT network due to the that its training is adapted using time differences if the IoT network size is small or Monte Carlo otherwise. To validate the proposed algorithm in an environment as close to reality as possible, we proposed a scenario based on a real IoT network, where we tested different malware propagation models. Different simulations varying the number of agents and nodes in the IoT network have been developed. The result of these simulations proves the efficiency and adaptability of the proposed algorithm in detecting malware, regardless of the malware propagation model.es_ES
dc.language.isoenges_ES
dc.publisherOxford University Presses_ES
dc.relation.urihttps://doi.org/10.1093/jigpal/jzae068es_ES
dc.subjectMalware propagationes_ES
dc.subjectNetwork discoveryes_ES
dc.subjectReinforcement learninges_ES
dc.titleMulti-agent reinforcement learning based algorithm detection of malware-infected nodes in IoT networkses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessinfo:eu-repo/semantics/embargoedAccesses_ES
dc.date.embargoEndDate2025-05-18es_ES
dc.date.embargoLift2025-05-18
UDC.journalTitleLogic Journal of the IGPLes_ES
dc.identifier.doihttps://doi.org/10.1093/jigpal/jzae068


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