Published in Future Generation Computer Systems, 152, 83-98, 2024
FederatedTrust, a framework that enhances trustworthiness in federated learning by addressing privacy, security, and accountability challenges.
Recommended citation: Sánchez Sánchez, Pedro Miguel et al. (2024). "FederatedTrust: A solution for trustworthy federated learning." Future Generation Computer Systems, 152, 83-98.
Download Paper
Published in Future Generation Computer Systems, 152, 30–42, 2024
Analyzes adversarial robustness of ML and hardware-based fingerprinting methods for IoT device authentication.
Recommended citation: Sánchez Sánchez, Pedro Miguel et al. (2024). "Adversarial attacks and defenses on ML-and hardware-based IoT device fingerprinting and identification." Future Generation Computer Systems, 152, 30–42.
Download Paper
Published in Journal of Network and Computer Applications, 103579, 2023
Proposes a methodology to uniquely identify SBCs using low-level hardware performance features and Machine Learning.
Recommended citation: Sánchez Sánchez, Pedro Miguel et al. (2023). "A methodology to identify identical single-board computers based on hardware behavior fingerprinting." Journal of Network and Computer Applications, 103579.
Download Paper
Published in IEEE Communications Magazine, 2022
Proposes SpecForce, a framework for protecting spectrum sensors against zero-day threats in battlefield IoT environments.
Recommended citation: Sánchez Sánchez, Pedro Miguel et al. (2022). "SpecForce: A Framework to Secure IoT Spectrum Sensors in the Internet of Battlefield Things." IEEE Communications Magazine.
Download Paper
Published in IEEE Communications Surveys & Tutorials, 23(2), 1048–1077, 2021
Comprehensive review of behavioral fingerprinting for IoT devices, including techniques, data sources, and application domains.
Recommended citation: Sánchez Sánchez, Pedro Miguel et al. (2021). "A survey on device behavior fingerprinting: Data sources, techniques, application scenarios, and datasets." IEEE Communications Surveys & Tutorials, 23(2), 1048–1077.
Download Paper
Published in IEEE International Joint Conference on Neural Networks (IJCNN) 2025, 2025
S-VOTE proposes a decentralized client selection strategy based on similarity voting to improve convergence and performance in non-IID federated learning scenarios.
Recommended citation: Sánchez Sánchez, Pedro Miguel et al. (2025). "S-VOTE: Similarity-based Voting for Client Selection in Decentralized Federated Learning." IEEE IJCNN 2025.
Download Paper
Published in IEEE International Conference on Communications (ICC) 2025, 2025
ProFe introduces a novel communication optimization algorithm for decentralized federated learning (DFL) that combines knowledge distillation, prototype learning, and quantization techniques to enhance efficiency and performance.
Recommended citation: Sánchez Sánchez, Pedro Miguel, Martínez Beltrán, Enrique Tomás, Fernández Llamas, Miguel, Bovet, Gérôme, Martínez Pérez, Gregorio, & Huertas Celdrán, Alberto. (2025). "ProFe: Communication-Efficient Decentralized Federated Learning via Distillation and Prototypes." IEEE ICC 2025.
Download Paper
Published in IEEE Military Communications Conference (MILCOM) 2024, 2024
This paper presents a novel framework leveraging pre-trained LLMs to classify malware based on system call data.
Recommended citation: Sánchez, P. M. S., Celdrán, A. H., Bovet, G., & Pérez, G. M. (2024, October). Transfer learning in pre-trained large language models for malware detection based on system calls. In MILCOM 2024-2024 IEEE Military Communications Conference (MILCOM) (pp. 853-858). IEEE.
Download Paper