Lorenzo Fernandez Maimo-rekin lankidetzan egindako argitalpenak (15)

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. 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. Intelligent and dynamic ransomware spread detection and mitigation in integrated clinical environments

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

  4. 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