Publications (49) Lorenzo Fernandez Maimo publications View referenced research data.

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2026

  1. A Scalable Framework for Multidimensional Quality Assessment and Benchmarking of EEG Datasets

    IEEE Access, Vol. 14, pp. 57850-57866

2025

  1. Detection of Adversarial Attacks Using Deep Learning and Features Extracted From Interpretability Methods in Industrial Scenarios

    IEEE Access, Vol. 13, pp. 2705-2722

  2. EEG channel reconstruction using convolutional neural networks in limited BCIs: A proposed method for neuromarketing applications

    Applied Soft Computing, Vol. 181

  3. ShaTS: a Shapley-based explainability method for time series artificial intelligence models

    Future Generation Computer Systems, Vol. 176

  4. Un modulo de explicabilidad basado en los valores Shapley para frameworks de deteccion de anomalías sobre datos secuenciales: temporal SHAP

    Actas de las X Jornadas Nacionales de Investigación en Ciberseguridad

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. Data fusion in neuromarketing: Multimodal analysis of biosignals, lifecycle stages, current advances, datasets, trends, and challenges

    Information Fusion, Vol. 105

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. AuthCODE: A privacy-preserving and multi-device continuous authentication architecture based on machine and deep learning

    Computers and Security, Vol. 103

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

    Procedia Computer Science

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

    Software - Practice and Experience, Vol. 51, Núm. 3, pp. 607-627

2020

  1. Anomaly Detection on Encrypted and High-Performance Data Networks by Means of Machine Learning Techniques

    Recent Advances in Security, Privacy, and Trust for Internet of Things (IoT) and Cyber-Physical Systems (CPS), pp. 167-190