Pedro Miguel Sánchez

Pedro Miguel Sánchez

Data Scientist and Researcher — Ph.D. in Computer Science. I currently work as a Data Scientist at Roche.

Posts by Collection

portfolio

publications

A survey on device behavior fingerprinting: Data sources, techniques, application scenarios, and datasets

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.
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A methodology to identify identical single-board computers based on hardware behavior fingerprinting

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

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.
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FederatedTrust: A solution for trustworthy federated learning

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.
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Transfer Learning in Pre-Trained Large Language Models for Malware Detection Based on System Calls

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.
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ProFe: Communication-Efficient Decentralized Federated Learning via Distillation and Prototypes

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.
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S-VOTE: Similarity-based Voting for Client Selection in Decentralized Federated Learning

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.
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talks

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post.

Teaching experience 2

Workshop, University 1, Department, 2015

This is a description of a teaching experience. You can use markdown like any other post.