Angel Luis Perales Gomez-rekin lankidetzan egindako argitalpenak (18)

2024

  1. A Review of An Interpretable Semi-Supervised System for Detecting Cyberattacks Using Anomaly Detection in Industrial Scenarios

    IX Jornadas Nacionales de Investigación En Ciberseguridad

  2. A Review of SUSAN: A Deep Learning based anomaly detection framework for sustainable industry

    IX Jornadas Nacionales de Investigación En Ciberseguridad

  3. A Review of VAASI: Crafting Valid and Abnormal Adversarial Samples for Anomaly Detection Systems in Industrial Scenarios

    IX Jornadas Nacionales de Investigación En Ciberseguridad

  4. Corrigendum to “Fedstellar: A platform for decentralized federated learning” [Expert Syst. Appl. 242 (2024) 122861] (S0957417423033638), (10.1016/j.eswa.2023.122861)

    Expert Systems with Applications

  5. Fedstellar: A Platform for Decentralized Federated Learning

    Expert Systems with Applications, Vol. 242

2023

  1. An interpretable semi-supervised system for detecting cyberattacks using anomaly detection in industrial scenarios

    IET Information Security, Vol. 17, Núm. 4, pp. 553-566

  2. Behavioral fingerprinting to detect ransomware in resource-constrained devices

    Computers and Security, Vol. 135

  3. SUSAN: A Deep Learning based anomaly detection framework for sustainable industry

    Sustainable Computing: Informatics and Systems, Vol. 37

  4. VAASI: Crafting valid and abnormal adversarial samples for anomaly detection systems in industrial scenarios

    Journal of Information Security and Applications, Vol. 79

2022

  1. A Methodology for Evaluating the Robustness of Anomaly Detectors to Adversarial Attacks in Industrial Scenarios

    IEEE Access, Vol. 10, pp. 124582-124594

  2. Malware Detection in Industrial Scenarios Using Machine Learning and Deep Learning Techniques

    Advances in Malware and Data-Driven Network Security, pp. 74-93

2021

  1. A Review of MADICS: A Methodology for Anomaly Detection in Industrial Control Systems

    Investigación en Ciberseguridad

  2. Crafting adversarial samples for anomaly detectors in industrial control systems

    Procedia Computer Science

  3. SafeMan: A unified framework to manage cybersecurity and safety in manufacturing industry

    Software - Practice and Experience

2020

  1. BEHACOM - a dataset modelling users’ behaviour in computers

    Data in Brief

  2. MADICS: A methodology for anomaly detection in industrial control systems

    Symmetry, Vol. 12, Núm. 10

2019

  1. Intelligent and Dynamic Ransomware Spread Detection and Mitigation in Integrated Clinical Environments

    Sensors (Basel, Switzerland), Vol. 19, Núm. 5

  2. On the Generation of Anomaly Detection Datasets in Industrial Control Systems

    IEEE Access, Vol. 7, pp. 177460-177473