A Self-Adaptive Deep Learning-Based System for Anomaly Detection in 5G Networks. - Lorenzo Fernández Maimó; Ángel Luis Perales Gómez; Félix J. García Clemente; Manuel Gil Pérez; Gregorio Martínez Pérez (IEEE Access, 2018)

On the Generation of Anomaly Detection Datasets in Industrial Control Systems. - Ángel Luis Perales Gómez; Lorenzo Fernández Maimó; Alberto Huertas Celdrán; Félix J. García Clemente; Cristian Cadenas Sarmiento; Carlos Javier Del Canto Masa; Rubén Méndez Nistal (IEEE Access, 2019)

Intelligent and Dynamic Ransomware Spread Detection and Mitigation in Integrated Clinical Environments. - Lorenzo Fernández Maimó; Alberto Huertas Celdrán; Ángel Luis Perales Gómez; Félix J. García Clemente; James Weimer; Insup Lee 001 (Sensors, 2019)

MADICS: A Methodology for Anomaly Detection in Industrial Control Systems. - Ángel Luis Perales Gómez; Lorenzo Fernández Maimó; Alberto Huertas Celdrán; Félix J. García Clemente (Symmetry, 2020)

BEHACOM. - Pedro Miguel Sánchez Sánchez; José María Jorquera Valero; Mattia Zago; Alberto Huertas Celdrán; Lorenzo Fernández Maimó; Eduardo López Bernal; Sergio López Bernal; Javier Martínez Valverde; Pantaleone Nespoli; Javier Pastor-Galindo; Ángel Luis Perales Gómez; Manuel Gil Pérez; et al. (IEEE DataPort, 2020)

Crafting Adversarial Samples for Anomaly Detectors in Industrial Control Systems. - Ángel Luis Perales Gómez; Lorenzo Fernández Maimó; Alberto Huertas Celdrán; Félix J. García Clemente; Frances Cleary (ANT/EDI40, 2021)

SafeMan: A unified framework to manage cybersecurity and safety in manufacturing industry. - Ángel Luis Perales Gómez; Lorenzo Fernández Maimó; Alberto Huertas Celdrán; Félix J. García Clemente; Manuel Gil Pérez; Gregorio Martínez Pérez (Softw. Pract. Exp., 2021)

A Methodology for Evaluating the Robustness of Anomaly Detectors to Adversarial Attacks in Industrial Scenarios. - Ángel Luis Perales Gómez; Lorenzo Fernández Maimó; Félix J. García Clemente; Javier Alejandro Maroto Morales; Alberto Huertas Celdrán; Gérôme Bovet (IEEE Access, 2022)

FARMIT: continuous assessment of crop quality using machine learning and deep learning techniques for IoT-based smart farming. - Ángel Luis Perales Gómez; Pedro E. López-de-Teruel; Alberto Ruiz; Ginés García-Mateos; Gregorio Bernabé García; Félix J. García Clemente (Clust. Comput., 2022)

TemporalFED: Detecting Cyberattacks in Industrial Time-Series Data Using Decentralized Federated Learning. - Ángel Luis Perales Gómez; Enrique Tomás Martínez Beltrán; Pedro Miguel Sánchez Sánchez; Alberto Huertas Celdrán (CoRR, 2023)

SUSAN: A Deep Learning based anomaly detection framework for sustainable industry. - Ángel Luis Perales Gómez; Lorenzo Fernández Maimó; Alberto Huertas Celdrán; Félix J. García Clemente (Sustain. Comput. Informatics Syst., 2023)

An interpretable semi-supervised system for detecting cyberattacks using anomaly detection in industrial scenarios. - Ángel Luis Perales Gómez; Lorenzo Fernández Maimó; Alberto Huertas Celdrán; Félix J. García Clemente (IET Inf. Secur., 2023)

Behavioral fingerprinting to detect ransomware in resource-constrained devices. - Alberto Huertas Celdrán; Pedro Miguel Sánchez Sánchez; Jan von der Assen; Dennis Shushack; Ángel Luis Perales Gómez; Gérôme Bovet; Gregorio Martínez Pérez; Burkhard Stiller (Comput. Secur., 2023)

VAASI: Crafting valid and abnormal adversarial samples for anomaly detection systems in industrial scenarios. - Ángel Luis Perales Gómez; Lorenzo Fernández Maimó; Alberto Huertas Celdrán; Félix J. García Clemente (J. Inf. Secur. Appl., 2023)

Enhacing Logistics with Computer Vision and Fog Computing-Driven Auto-ID Technologies. - Juan Jesús Losada Del Olmo; Ángel Luis Perales Gómez; Pedro E. López-de-Teruel Alcolea; Alberto Ruiz García; Félix J. García Clemente (FMEC, 2024)

Advancing Threat Detection in Fog Computing: A Comprehensive Approach to Real-Time Analysis and Model Generation. - Lucía Cuadrillero Paredes; Ángel Luis Perales Gómez; Óscar Cánovas; Lorenzo Fernández Maimó; Félix J. García Clemente (FMEC, 2024)

MeloDI: An Internet of Things Architecture to Evaluate Melon Quality by Means of Machine Learning Using Sensors Data and Drone Images. - Ángel Luis Perales Gómez; Juan Jesús Losada Del Olmo; Pedro E. López-de-Teruel; Alberto Ruiz; Félix J. García Clemente; Andrés Conesa Bueno (IEEE Access, 2024)

A few-shot learning methodology for improving safety in industrial scenarios through universal self-supervised visual features and dense optical flow. - Juan Jesús Losada Del Olmo; Ángel Luis Perales Gómez; Pedro E. López-de-Teruel; Alberto Ruiz (Appl. Soft Comput., 2024)

Corrigendum to "Fedstellar: A platform for decentralized federated learning" [Expert Syst. Appl. 242 (2024) 122861]. - Enrique Tomás Martínez Beltrán; Ángel Luis Perales Gómez; Chao Feng 001; Pedro Miguel Sánchez Sánchez; Pedro Guijas Bravo; Sergio López Bernal; Gérôme Bovet; Manuel Gil Pérez; Gregorio Martínez Pérez; Alberto Huertas Celdrán (Expert Syst. Appl., 2024)

Fedstellar: A Platform for Decentralized Federated Learning. - Enrique Tomás Martínez Beltrán; Ángel Luis Perales Gómez; Chao Feng 001; Pedro Miguel Sánchez Sánchez; Sergio López Bernal; Gérôme Bovet; Manuel Gil Pérez; Gregorio Martínez Pérez; Alberto Huertas Celdrán (Expert Syst. Appl., 2024)

Detection of Adversarial Attacks Using Deep Learning and Features Extracted From Interpretability Methods in Industrial Scenarios. - Ángel Luis Perales Gómez; Lorenzo Fernández Maimó; Alberto Huertas Celdrán; Félix J. García Clemente (IEEE Access, 2025)

ShaTS: A Shapley-based Explainability Method for Time Series Artificial Intelligence Models applied to Anomaly Detection in Industrial Internet of Things. - Manuel Franco de la Peña; Ángel Luis Perales Gómez; Lorenzo Fernández Maimó (CoRR, 2025)

Reporte de vulnerabilidades en IIoT. Proyecto DEFENDER. - Pedro Almansa Jiménez; Lorenzo Fernández Maimó; Ángel Luis Perales Gómez (CoRR, 2025)

Simulating Cyberattacks through a Breach Attack Simulation (BAS) Platform empowered by Security Chaos Engineering (SCE). - Arturo Sánchez-Matas; Pablo Escribano Ruiz; Daniel Díaz López; Ángel Luis Perales Gómez; Pantaleone Nespoli; Gregorio Martínez Pérez (CoRR, 2025)

DEFENDIFY: defense amplified with transfer learning for obfuscated malware framework. - Rodrigo Castillo Camargo; Juan Murcia Nieto; Nicolás Rojas 004; Daniel Díaz López; Santiago Alférez; Ángel Luis Perales Gómez; Pantaleone Nespoli; Félix Gómez Mármol; Umit Karabiyik (Cybersecur., 2025)