Fernández-Morales, EnriqueTobarra Abad, María de los LlanosRobles Gómez, AntonioPastor Vargas, RafaelHernández Berlinches, RobertoSarraipa, Joao2025-10-242025-10-242025-10-20Enrique Fernández-Moralesa, Llanos Tobarra, Antonio Robles-Gómez, Rafael Pastor-Vargas, Roberto Hernández, Joao Sarraipa. eXplicability AI (XAI) for Attack Detection toward Smart Rural Applications. Internet of Things; Engineering Cyber Physical Human Systems. 2025.2542-6605https://doi.org/10.1016/j.iot.2025.101804https://hdl.handle.net/20.500.14468/30615The registered version of this article, first published in Internet of Things, is available online at the publisher's website: Elsevier, https://doi.org/10.1016/j.iot.2025.101804La versión registrada de este artículo, publicado por primera vez en Internet of Things, está disponible en línea en el sitio web del editor: Elsevier, https://doi.org/10.1016/j.iot.2025.101804Cátedra Internacional "Smart Rural IoT and Secured Environments" (C56.23). Financiado por INCIBE en el contexto del Plan de Recuperación, Transformación y Resiliencia de la Unión Europea (NextGenerationEU/PRTR)This research evaluates the performance and computational efficiency of various AI models for intrusion detection in IoT environments, with the goal of enabling future deployment in Smart Rural scenarios. Leveraging the massive NF-UQ-NIDS-v2 dataset-comprising over 76 million labeled NetFlow records across 21 traffic classes-we benchmark five models, ranging from classical machine learning algorithms to deep learning architectures, across both high-performance and low-performance execution setups. The analysis covers standard classification metrics (accuracy, precision, recall, F1-score) and detailed resource usage indicators, including inference time, memory footprint, CPU cycles, and energy consumption per batch. Additionally, explainable AI techniques (SHAP and LIME) are employed to investigate model behavior and feature relevance under real-world constraints. Results show that classical models, particularly Random Forest and Decision Tree, achieve top-tier detection accuracy while maintaining minimal computational demands, making them strong candidates for constrained deployments. Deep learning models deliver comparable predictive performance but incur significantly higher resource consumption, requiring further optimization for practical use. Overall, this work provides a comprehensive evaluation framework and practical insights for selecting efficient and interpretable AI-based intrusion detection systems for rural and low-resource infrastructures.eninfo:eu-repo/semantics/embargoedAccess1203.04 Inteligencia artificialartículoInternet of Things (IoT)cybersecurityanomaly detectiondeep learning,eXplicability AI (XAI)