Fernández Morales, EnriqueTobarra Abad, María de los LlanosRobles Gómez, AntonioPastor Vargas, RafaelVidal Balboa, PedroSarraipa, Joao2025-05-122025-05-122025-06Enrique Fernández-Morales, Llanos Tobarra, Antonio Robles-Gómez, Rafael Pastor-Vargas, Pedro Vidal-Balboa, Joao Sarraipa. (2025); Título: Anomaly Detection in Smart Rural IoT Systems; Publicación: DCAI - Main Track. ISSN: 2172-766X; Páginas 1-102172-766Xhttps://hdl.handle.net/20.500.14468/26533This is the Accepted Manuscript of an article that will soon be published by Springer in "Main Track", when the conference proceedings are published. Este es el manuscrito aceptado de un artículo que Springer publicará próximamente en "Main Track", cuando se publiquen las actas de la conferencia.The main contribution of this work is based on the presentation of new AI models for the detection of attacks, within IoT systems using an extensive and complete dataset. In this context, we evaluate not only the performance of the models in terms of detection of attacks, but also their resource consumption, such as the time needed to analyze a sample, the consumption of computing cycles to analyze a sample, as well as the hard disk usage to store the AI models. Its application is oriented to the context of IoT systems in rural environments, where devices deployed in these environments usually have strong restrictions on these resources. Our results indicate that the OPTIMIST-LSTM model offers the best balance between accuracy and generalization, whereas XAI-IoT stands out for its computational efficiency, making them the most suitable for implementation in IoT infrastructures with limited resources.eninfo:eu-repo/semantics/embargoedAccess33 Ciencias Tecnológicasactas de congresoInternet of Things (IoT)smart RuralcybersecurityArtificial Intelligence (AI)Intrusion Detection System (IDS)