Publicacions en col·laboració amb investigadors/es de University of Zurich (71)

2024

  1. Adversarial attacks and defenses on ML- and hardware-based IoT device fingerprinting and identification

    Future Generation Computer Systems, Vol. 152, pp. 30-42

  2. Analyzing the robustness of decentralized horizontal and vertical federated learning architectures in a non-IID scenario

    Applied Intelligence, Vol. 54, Núm. 8, pp. 6637-6653

  3. 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

  4. CyberSpec: Behavioral Fingerprinting for Intelligent Attacks Detection on Crowdsensing Spectrum Sensors

    IEEE Transactions on Dependable and Secure Computing, Vol. 21, Núm. 1, pp. 284-297

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

    Information Fusion, Vol. 105

  6. Evaluating the impact of contextual information on the performance of intelligent continuous authentication systems

    ACM International Conference Proceeding Series

  7. FederatedTrust: A solution for trustworthy federated learning

    Future Generation Computer Systems, Vol. 152, pp. 83-98

  8. Fedstellar: A Platform for Decentralized Federated Learning

    Expert Systems with Applications, Vol. 242

  9. Impact of neural cyberattacks on a realistic neuronal topology from the primary visual cortex of mice

    Wireless Networks

  10. Mitigating communications threats in decentralized federated learning through moving target defense

    Wireless Networks

  11. NeuronLab: BCI framework for the study of biosignals

    Neurocomputing, Vol. 598

  12. Paired plasma lipidomics and proteomics analysis in the conversion from mild cognitive impairment to Alzheimer's disease

    Computers in Biology and Medicine, Vol. 176

  13. Privacy-preserving hierarchical federated learning with biosignals to detect drowsiness while driving

    Neural Computing and Applications, Vol. 36, Núm. 32, pp. 20425-20437

  14. RL and Fingerprinting to Select Moving Target Defense Mechanisms for Zero-Day Attacks in IoT

    IEEE Transactions on Information Forensics and Security, Vol. 19, pp. 5520-5529

  15. Robust Federated Learning for execution time-based device model identification under label-flipping attack

    Cluster Computing, Vol. 27, Núm. 1, pp. 313-324

  16. Single-board device individual authentication based on hardware performance and autoencoder transformer models

    Computers and Security, Vol. 137

  17. Studying Drowsiness Detection Performance While Driving Through Scalable Machine Learning Models Using Electroencephalography

    Cognitive Computation, Vol. 16, Núm. 3, pp. 1253-1267

  18. Studying the Robustness of Anti-Adversarial Federated Learning Models Detecting Cyberattacks in IoT Spectrum Sensors

    IEEE Transactions on Dependable and Secure Computing, Vol. 21, Núm. 2, pp. 573-584

2023

  1. A Framework Quantifying Trustworthiness of Supervised Machine and Deep Learning Models

    CEUR Workshop Proceedings

  2. A Lightweight Moving Target Defense Framework for Multi-purpose Malware Affecting IoT Devices

    IEEE International Conference on Communications