Pedro Miguel Sanchez Sanchez

Pedro Miguel Sanchez Sanchez is a researcher affiliated with the University of Murcia, specializing in Machine Learning, Cybersecurity, and IoT. He earned his doctorate in 2024 with the thesis Identical IoT device identification via hardware performance fingerprinting and Machine Learning, supervised by Dr. Alberto Huertas Celdrán and Dr. Gregorio Martínez Pérez.

Research Interests

Pedro's work focuses on applying Machine Learning and Federated Learning to solve critical challenges in Cybersecurity and IoT environments. His research includes:

• Developing decentralized federated learning frameworks (e.g., Flighter, ProFe) for secure and efficient model training.
• Designing malware detection systems using system call data and large language models.
• Enhancing IoT device authentication via hardware fingerprinting techniques.
• Exploring moving target defense strategies to counter zero-day attacks on IoT networks.
• Building knowledge graphs for cyber defense applications.

Recent Publications

Pedro's 2025–2024 publications demonstrate a strong focus on decentralized federated learning, malware mitigation, and hardware-based security. Key trends include:

• Advancements in zero-shot learning for multilingual tasks on edge devices.
• Security frameworks (e.g., Cyberforce, Sentinel) for military reconnaissance and industrial IoT.
• Behavioral analysis techniques for ransomware detection and continuous authentication.
• Robustness studies on federated learning architectures under adversarial conditions.
• Creation of benchmarks like LwHBench for hardware performance evaluation.

Collaborations

He has collaborated with researchers in the Intelligent Systems and Telematics group, contributing to projects in 5G security, crowdsensing platforms, and trusted execution environments.