Angel Luis Perales Gomez-rekin lankidetzan egindako argitalpenak (20)

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. Advancing Threat Detection in Fog Computing: A Comprehensive Approach to Real-Time Analysis and Model Generation

    2024 9th International Conference on Fog and Mobile Edge Computing, FMEC 2024

  5. Enhacing Logistics with Computer Vision and Fog Computing-Driven Auto-ID Technologies

    2024 9th International Conference on Fog and Mobile Edge Computing, FMEC 2024

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

    Sustainable Computing: Informatics and Systems, Vol. 37

  3. 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. FARMIT: continuous assessment of crop quality using machine learning and deep learning techniques for IoT-based smart farming

    Cluster Computing, Vol. 25, Núm. 3, pp. 2163-2178

  3. 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. A Deep Learning-based System for Network Cyber Threat Detection

    Machine Learning for Computer and Cyber Security, pp. 1-25

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

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

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

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

2018

  1. A Self-Adaptive Deep Learning-Based System for Anomaly Detection in 5G Networks

    IEEE Access, Vol. 6, pp. 7700-7712