Publications
On the Practical Viability of Local Agentic Language Models for Android Security Analysis
Padín-Torrente, H., & Ortega-Fernandez, I. (2026). On the Practical Viability of Local Agentic Language Models for Android Security Analysis. Poster presentation at Jornadas Nacionales de Investigación en Ciberseguridad (JNIC 2026).
neuralGAM: An R Package for fitting Generalized Additive Neural Networks
Ortega-Fernandez, I., & Sestelo, M. (2026). neuralGAM: An R Package for fitting Generalized Additive Neural Networks. The R Journal. https://doi.org/10.5281/zenodo.10964608
Toward human-centered explainability: Natural language explanations for anomaly detection
Padín-Torrente, H., Carneiro-Diaz, V., & Ortega-Fernandez, I. (2026). Toward human-centered explainability: Natural language explanations for anomaly detection. Information Systems Frontiers, 1–17. https://doi.org/10.1007/s10796-026-10717-3
Towards Explainable AI: Neural Network-Based Training of Generalized Additive Models
Ortega-Fernández, I., Sestelo, M., & Villanueva, N. M. (2025). Towards Explainable AI: Neural Network-Based Training of Generalized Additive Models. Reviewed contribution accepted for conference presentation at RSS 2025.
An efficient gradient-based inference attack for federated learning
Montaña-Fernández, P., & Ortega-Fernandez, I. (2025). An efficient gradient-based inference attack for federated learning. arXiv preprint arXiv:2512.15143.
Advanced Detection of Suspicious Activity within UEBA Framework using Deep Autoencoders
Fuentes Rodríguez, J., Ortega-Fernandez, I., Villanueva, N. M., & Sestelo, M. (2025). Advanced Detection of Suspicious Activity within UEBA Framework using Deep Autoencoders. Oral communication at XVII Congreso Galego de Estatística e Investigación de Operacións.
Combining Neural Networks and Generalized Additive Models for Explainable AI
Ortega-Fernandez, I., Sestelo, M., & Villanueva, N. M. (2025). Combining Neural Networks and Generalized Additive Models for Explainable AI. Poster presentation at XVII Congreso Galego de Estatística e Investigación de Operacións.
The future of law enforcement: How PRESERVE’s AI and big data solutions benefit public safety
Makri, F., Spantideas, S., Kokkinis, G., Ortega-Fernandez, I., Alonso Doval, P., & Varveris, M. (2025). The future of law enforcement: How PRESERVE’s AI and big data solutions benefit public safety. Poster presentation at the 6th International Conference in Electronic Engineering & Information Technology (EEITE).
Cybersecurity threat detection based on a UEBA framework using deep autoencoders
Fuentes, J., Ortega-Fernandez, I., Villanueva, N. M., & Sestelo, M. (2025). Cybersecurity threat detection based on a UEBA framework using deep autoencoders. AIMS Mathematics, 10(10), 23496–23517. https://doi.org/10.3934/math.20251043
Machine Learning Approaches and Explainability for Real-Time Cyberattack Detection
I. Ortega-Fernandez (2024). Machine Learning Approaches and Explainability for Real-Time Cyberattack Detection. Doctoral thesis, University of Vigo.
Enhancing Privacy in Federated Learning: A Practical Assessment of Combined PETs in a Cross-Silo Setting
Loureiro-Acuña, J., Martínez-Luaña, X., Padín-Torrente, H., Jiménez-Balsa, G., García-Pagán, C., & Ortega-Fernandez, I. (2024). Enhancing Privacy in Federated Learning: A Practical Assessment of Combined PETs in a Cross-Silo Setting. Proceedings of the 2024 ACM Workshop on Information Hiding and Multimedia Security, 265–270. doi:10.1145/3658664.3659661
Explainable deep learning: A methodology to train Generalized Additive Models with deep neural networks
Ortega-Fernandez, I., & Sestelo, M. (2024). Explainable deep learning: A methodology to train Generalized Additive Models with deep neural networks. Poster presentation at ISNPS 2024.
WhatTheFile, análisis forense basado en Inteligencia Artificial
Pintos, B., & Ortega-Fernandez, I. (2024). WhatTheFile, análisis forense basado en Inteligencia Artificial. In IX Jornadas Nacionales de Investigación En Ciberseguridad (pp. 600-605). Antonia M. Reina Quintero.
Development of a modular virtual Industrial Control System prototype for cybersecurity research
Carnero Ortega, D., Piñón Blanco, C., Pintos Castro, B., & Ortega Fernández, I. (2024). Development of a modular virtual Industrial Control System prototype for cybersecurity research. IX Jornadas Nacionales de Investigación En Ciberseguridad, 534-539.
Explainable Generalized Additive Neural Networks with Independent Neural Network Training
Ortega-Fernández, I., & Sestelo, M. (2023). Explainable Generalized Additive Neural Networks with Independent Neural Network Training. Poster presentation at CFE-CMStatistics 2023.
Explainable generalized additive neural networks with independent neural network training
Ortega-Fernandez, I., Sestelo, M. & Villanueva, N.M. Explainable generalized additive neural networks with independent neural network training. Stat Comput 34, 6 (2024). https://doi.org/10.1007/s11222-023-10320-5
Hexanonymity: a scalable geo-positioned data clustering algorithm for anonymisation purposes
J. Rodriguez-Viñas, I. Ortega-Fernandez and E. S. Martínez, "Hexanonymity: a scalable geo-positioned data clustering algorithm for anonymisation purposes," 2023 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW), Delft, Netherlands, 2023, pp. 396-404, doi: 10.1109/EuroSPW59978.2023.00050.
Detección de bots avanzados en comercio electrónico: un caso de uso real
Mauro Saavedra-Golán, Ines Ortega-Fernandez (2023, 22 de junio). Detección de bots avanzados en comercio electrónico: un caso de uso real. Jornadas Nacionales de Investigación en Ciberseguridad [Conferencia]. Vigo, España. ISBN 978-84-8158-970-2, págs. 229-235
Detecting Anomalies in Industrial Control Systems with LSTM Neural Networks and UEBA
C. Piñón-Blanco, F. Otero-Vázquez, I. Ortega-Fernandez and M. Sestelo, "Detecting Anomalies in Industrial Control Systems with LSTM Neural Networks and UEBA," 2023 JNIC Cybersecurity Conference (JNIC), Vigo, Spain, 2023, pp. 1-8, doi: 10.23919/JNIC58574.2023.10205609.
Network intrusion detection system for DDoS attacks in ICS using deep autoencoders
Ortega-Fernandez, I., Sestelo, M., Burguillo, J.C. et al. Network intrusion detection system for DDoS attacks in ICS using deep autoencoders. Wireless Netw (2023). https://doi.org/10.1007/s11276-022-03214-3
A Review of Denial of Service Attack and Mitigation in the Smart Grid Using Reinforcement Learning
Ortega-Fernandez, Ines, and Francesco Liberati. 2023. "A Review of Denial of Service Attack and Mitigation in the Smart Grid Using Reinforcement Learning" Energies 16, no. 2: 635. https://www.mdpi.com/1996-1073/16/2/635
Large Scale Data Anonymisation for GDPR Compliance
Ortega-Fernandez, I., Martinez, S.E.K., Orellana, L.A. (2022). Large Scale Data Anonymisation for GDPR Compliance. In: Soldatos, J., Kyriazis, D. (eds) Big Data and Artificial Intelligence in Digital Finance. Springer, Cham. https://doi.org/10.1007/978-3-030-94590-9_19
How to implement EU data protection regulation for R&D in biometrics
R. Sanchez-Reillo, I. Ortega-Fernandez, W. Ponce-Hernandez, and H. C. Quiros-Sandoval, “How to implement EU data protection regulation for R&D in biometrics,” Comput. Stand. Interfaces, vol. 61, 2019, doi: 10.1016/j.csi.2018.01.007.
How to implement EU data protection regulation for R&D on personal data
R. Sanchez-Reillo, I. Ortega-Fernandez, W. Ponce-Hernandez, and H. C. Quiros-Sandoval, “How to implement EU data protection regulation for R&D on personal data,” in Proceedings - International Carnahan Conference on Security Technology, 2017, vol. 2017-Octob, doi: 10.1109/CCST.2017.8167797.