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

publications

Improving the Security and QoE in Mobile Devices through an Intelligent and Adaptive Continuous Authentication System

Published in Sensors, 18(11), 3769, 2018

Continuous authentication systems for mobile devices focus on identifying users according to their behaviour patterns when they interact with mobile devices. Among the benefits provided by these systems, we highlight…

Recommended citation: Jorquera Valero, José María, Sánchez Sánchez, Pedro Miguel, Fernández Maimó, Lorenzo, Huertas Celdrán, Alberto, Arjona Fernández, Marcos, De Los Santos Vílchez, Sergio, & Martínez Pérez, Gregorio. (2018). "Improving the Security and QoE in Mobile Devices through an Intelligent and Adaptive Continuous Authentication System." Sensors, 18(11), 3769.
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Securing Smart Offices Through an Intelligent and Multi-device Continuous Authentication System

Published in Communications in Computer and Information Science, 2019

Smart Offices promise the improvement of working conditions in terms of efficiency, productivity and facility. However, new cybersecurity challenges arise associated with the new capabilities of Smart Cities. One of the…

Recommended citation: Sánchez Sánchez, Pedro Miguel, Huertas Celdrán, Alberto, Fernández Maimó, Lorenzo, Martínez Pérez, Gregorio, & Wang, Guojun. (2019). "Securing Smart Offices Through an Intelligent and Multi-device Continuous Authentication System." Communications in Computer and Information Science.
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BEHACOM - a dataset modelling users’ behaviour in computers

Published in Data in Brief, 31, 105767, 2020

This paper details the methodology and approach conducted to monitor the behaviour of twelve users interacting with their computers for fifty-five consecutive days without preestablished indications or restrictions. The…

Recommended citation: Sánchez Sánchez, Pedro Miguel, Jorquera Valero, José María, Zago, Mattia, Huertas Celdrán, Alberto, Fernández Maimó, Lorenzo, López Bernal, Eduardo, López Bernal, Sergio, Martínez Valverde, Javier, Nespoli, Pantaleone, Pastor Galindo, Javier, Perales Gómez, Ángel Luis, Gil Pérez, Manuel, & Martínez Pérez, Gregorio. (2020). "BEHACOM - a dataset modelling users’ behaviour in computers." Data in Brief, 31, 105767.
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AI and ML – Enablers for Beyond 5G Networks

Published in 5G-PPP Technology Board White Paper (Zenodo), 2020

White paper of the 5G-PPP Technology Board on AI and ML as enablers of 5G and beyond-5G networks, with contributions from 5G PPP projects.

Recommended citation: 5G-PPP Technology Board (Kaloxylos, Alexandros, Gavras, Anastasius, Camps Mur, Daniel, Ghoraishi, Mir, & Hrasnica, Halid, et al.). (2020). "AI and ML – Enablers for Beyond 5G Networks." 5G-PPP Technology Board White Paper, Zenodo.
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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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AuthCODE: A privacy-preserving and multi-device continuous authentication architecture based on machine and deep learning

Published in Computers & Security, 103, 102168, 2021

The authentication field is evolving towards mechanisms able to keep users continuously authenticated without the necessity of remembering or possessing authentication credentials. While existing continuous…

Recommended citation: Sánchez Sánchez, Pedro Miguel, Fernández Maimó, Lorenzo, Huertas Celdrán, Alberto, & Martínez Pérez, Gregorio. (2021). "AuthCODE: A privacy-preserving and multi-device continuous authentication architecture based on machine and deep learning." Computers & Security, 103, 102168.
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Design of a Security and Trust Framework for 5G Multi-domain Scenarios

Published in Journal of Network and Systems Management, 30(1), 7, 2021

With the expansion of 5G networks, new business models are arising where multi-tenancy and active infrastructure sharing will be key enablers for them. With these new opportunities, new security risks are appearing in…

Recommended citation: Jorquera Valero, José María, Sánchez Sánchez, Pedro Miguel, Lekidis, Alexios, Fernández Hidalgo, Javier, Gil Pérez, Manuel, Siddiqui, M. Shuaib, Huertas Celdrán, Alberto, & Martínez Pérez, Gregorio. (2021). "Design of a Security and Trust Framework for 5G Multi-domain Scenarios." Journal of Network and Systems Management, 30(1), 7.
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Trusted Execution Environment-Enabled Platform for 5G Security and Privacy Enhancement

Published in Studies in Big Data, 2021

With the deployment of 5G networks and the beginning of the design of beyond 5G communications, new critical requirements are emerging in terms of performance, security, and trust for leveraged technologies, such as…

Recommended citation: Valero, José María Jorquera, Sánchez Sánchez, Pedro Miguel, Lekidis, Alexios, Martins, Pedro, Diogo, Pedro, Pérez, Manuel Gil, Celdrán, Alberto Huertas, & Pérez, Gregorio Martínez. (2021). "Trusted Execution Environment-Enabled Platform for 5G Security and Privacy Enhancement." Studies in Big Data.
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Creation of a Dataset Modeling the Behavior of Malware Affecting the Confidentiality of Data Managed by IoT Devices

Published in Studies in Computational Intelligence, 2022

Internet-of-Things (IoT) platforms are vulnerable to cyberattacks due to their exposition to the internet and the resource-constrained capabilities of their devices. Cyberattacks can be conducted by different malware…

Recommended citation: Huertas Celdrán, Alberto, Sánchez Sánchez, Pedro Miguel, Sisi, Fabio, Bovet, Gérôme, Martínez Pérez, Gregorio, & Stiller, Burkhard. (2022). "Creation of a Dataset Modeling the Behavior of Malware Affecting the Confidentiality of Data Managed by IoT Devices." Studies in Computational Intelligence.
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Federated learning for malware detection in IoT devices

Published in Computer Networks, 204, 108693, 2022

Federated learning framework for IoT malware detection on N-BaIoT, including an analysis of poisoning attacks and robust aggregation. Computer Networks 2024 Best Paper Award.

Recommended citation: Rey, Valerian, Sánchez Sánchez, Pedro Miguel, Huertas Celdrán, Alberto, Bovet, Gérôme, & Jaggi, Martin. (2022). "Federated learning for malware detection in IoT devices." Computer Networks, 204, 108693.
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Toward pre-standardization of reputation-based trust models beyond 5G

Published in Computer Standards & Interfaces, 81, 103596, 2022

In the last years, the number of connections in mobile telecommunication networks has increased rampantly, and in consequence, the number and type of relationships among entities. Should such interactions are to be…

Recommended citation: Jorquera Valero, José María, Sánchez Sánchez, Pedro Miguel, Gil Pérez, Manuel, Huertas Celdrán, Alberto, & Martínez Pérez, Gregorio. (2022). "Toward pre-standardization of reputation-based trust models beyond 5G." Computer Standards & Interfaces, 81, 103596.
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Policy-based and Behavioral Framework to Detect Ransomware Affecting Resource-constrained Sensors

Published in NOMS 2022-2022 IEEE/IFIP Network Operations and Management Symposium, 1-7, 2022

Traditionally, data centers have been the preferred target for ransomware attacks. However, the increasing number of IoT (Internet-of-Things) devices managing valuable data is attracting the attention of cybercriminals…

Recommended citation: Huertas Celdrán, Alberto, Sánchez Sánchez, Pedro Miguel, Scheid, Eder J., Besken, Timuçin, Bovet, Gérôme, Martínez Pérez, Gregorio, & Stiller, Burkhard. (2022). "Policy-based and Behavioral Framework to Detect Ransomware Affecting Resource-constrained Sensors." NOMS 2022-2022 IEEE/IFIP Network Operations and Management Symposium, 1-7.
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Intelligent Fingerprinting to Detect Data Leakage Attacks on Spectrum Sensors

Published in ICC 2022 - IEEE International Conference on Communications, 4080-4085, 2022

Data confidentiality protection is a must for IoT and crowdsensing platforms, and a challenge due to the constrained nature of their sensors. Currently, the combination of device fingerprinting and anomaly detection…

Recommended citation: Huertas Celdrán, Alberto, Sánchez Sánchez, Pedro Miguel, Bovet, Gérôme, Martínez Pérez, Gregorio, & Stiller, Burkhard. (2022). "Intelligent Fingerprinting to Detect Data Leakage Attacks on Spectrum Sensors." ICC 2022 - IEEE International Conference on Communications, 4080-4085.
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Intelligent and behavioral-based detection of malware in IoT spectrum sensors

Published in International Journal of Information Security, 22(3), 541-561, 2022

The number of Cyber-Physical Systems (CPS) available in industrial environments is growing mainly due to the evolution of the Internet-of-Things (IoT) paradigm. In such a context, radio frequency spectrum sensing in…

Recommended citation: Celdrán, Alberto Huertas, Sánchez Sánchez, Pedro Miguel, Azorín Castillo, Miguel, Bovet, Gérôme, Martínez Pérez, Gregorio, & Stiller, Burkhard. (2022). "Intelligent and behavioral-based detection of malware in IoT spectrum sensors." International Journal of Information Security, 22(3), 541-561.
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RITUAL: a Platform Quantifying the Trustworthiness of Supervised Machine Learning

Published in 2022 18th International Conference on Network and Service Management (CNSM), 364-366, 2022

This demo presents RITUAL, a platform composed of a novel algorithm and a Web application quantifying the trustworthiness level of supervised Machine and Deep Learning (ML/DL) models according to their fairness…

Recommended citation: Huertas Celdrán, Alberto, Bauer, Jan, Demirci, Melike, Leupp, Joel, Franco, Muriel Figueredo, Sánchez Sánchez, Pedro Miguel, Bovet, Gérôme, Martínez Pérez, Gregorio, & Stiller, Burkhard. (2022). "RITUAL: a Platform Quantifying the Trustworthiness of Supervised Machine Learning." 2022 18th International Conference on Network and Service Management (CNSM), 364-366.
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Robust Federated Learning for execution time-based device model identification under label-flipping attack

Published in Cluster Computing, 27(1), 313-324, 2023

The computing device deployment explosion experienced in recent years, motivated by the advances of technologies such as Internet-of-Things (IoT) and 5G, has led to a global scenario with increasing cybersecurity risks…

Recommended citation: Sánchez Sánchez, Pedro Miguel, Huertas Celdrán, Alberto, Buendía Rubio, José Rafael, Bovet, Gérôme, & Martínez Pérez, Gregorio. (2023). "Robust Federated Learning for execution time-based device model identification under label-flipping attack." Cluster Computing, 27(1), 313-324.
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Cutting-Edge Assets for Trust in 5G and Beyond: Requirements, State of the Art, Trends, and Challenges

Published in ACM Computing Surveys, 55(11), 1-36, 2023

In 5G and beyond, the figure of cross-operator/domain connections and relationships grows exponentially among stakeholders, resources, and services, with reputation-based trust models being one of the capital…

Recommended citation: Jorquera Valero, José María, Sánchez Sánchez, Pedro Miguel, Gil Pérez, Manuel, Huertas Celdrán, Alberto, & Martínez Pérez, Gregorio. (2023). "Cutting-Edge Assets for Trust in 5G and Beyond: Requirements, State of the Art, Trends, and Challenges." ACM Computing Surveys, 55(11), 1-36.
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A Framework Quantifying Trustworthiness of Supervised Machine and Deep Learning Models

Published in SafeAI 2023: The AAAI Workshop on Artificial Intelligence Safety, 2023

An algorithm and web application that quantify the trustworthiness of supervised ML/DL models according to fairness, explainability, robustness, and accountability.

Recommended citation: Huertas Celdrán, Alberto, Kreischer, Jan, Demirci, Melike, Leupp, Joel, Sánchez Sánchez, Pedro Miguel, et al. (2023). "A Framework Quantifying Trustworthiness of Supervised Machine and Deep Learning Models." In SafeAI 2023: The AAAI Workshop on Artificial Intelligence Safety, CEUR Workshop Proceedings, 3381.
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Early Detection of Cryptojacker Malicious Behaviors on IoT Crowdsensing Devices

Published in NOMS 2023-2023 IEEE/IFIP Network Operations and Management Symposium, 1-8, 2023

Traditionally, IoT crowdsensing devices have been outside the cryptomining domain due to their limitations in terms of computational power. In 2014, Monero (XNR) changed this situation forever. Monero is an open-source…

Recommended citation: Celdrán, Alberto Huertas, von der Assen, Jan, Moser, Konstantin, Sánchez Sánchez, Pedro Miguel, Bovet, Gérôme, Martínez Pérez, Gregorio, & Stiller, Burkhard. (2023). "Early Detection of Cryptojacker Malicious Behaviors on IoT Crowdsensing Devices." NOMS 2023-2023 IEEE/IFIP Network Operations and Management Symposium, 1-8.
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Privacy-Preserving and Syscall-Based Intrusion Detection System for IoT Spectrum Sensors Affected by Data Falsification Attacks

Published in IEEE Internet of Things Journal, 10(10), 8408-8415, 2023

Crowdsensing platforms collect, process, transmit, and analyze spectrum data worldwide to optimize radio frequency spectrum usage. However, Internet of Things (IoT) spectrum sensors, performing some of the previous…

Recommended citation: Huertas Celdrán, Alberto, Sánchez Sánchez, Pedro Miguel, Feng, Chao, Bovet, Gérôme, Martínez Pérez, Gregorio, & Stiller, Burkhard. (2023). "Privacy-Preserving and Syscall-Based Intrusion Detection System for IoT Spectrum Sensors Affected by Data Falsification Attacks." IEEE Internet of Things Journal, 10(10), 8408-8415.
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A Lightweight Moving Target Defense Framework for Multi-purpose Malware Affecting IoT Devices

Published in ICC 2023 - IEEE International Conference on Communications, 6010-6015, 2023

Malware affecting Internet of Things (IoT) devices is rapidly growing due to the relevance of this paradigm in real-world scenarios. Specialized literature has also detected a trend towards multi-purpose malware able to…

Recommended citation: von der Assen, Jan, Celdrán, Alberto Huertas, Sánchez Sánchez, Pedro Miguel, Cedeño, Jordan, Bovet, Gérôme, Martínez Pérez, Gregorio, & Stiller, Burkhard. (2023). "A Lightweight Moving Target Defense Framework for Multi-purpose Malware Affecting IoT Devices." ICC 2023 - IEEE International Conference on Communications, 6010-6015.
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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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A Trustworthy Federated Learning Framework for Individual Device Identification

Published in 2023 JNIC Cybersecurity Conference (JNIC), 1-8, 2023

IoT scenarios face cybersecurity concerns due to unauthorized devices that can impersonate legitimate ones by using identical software and hardware configurations. This can lead to sensitive information leaks, data…

Recommended citation: Sánchez Sánchez, Pedro Miguel, Huertas Celdrán, Alberto, Bovet, Gérôme, Martínez Pérez, Gregorio, & Stiller, Burkhard. (2023). "A Trustworthy Federated Learning Framework for Individual Device Identification." 2023 JNIC Cybersecurity Conference (JNIC), 1-8.
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European framework and proofs-of-concept for the intelliGent aUtomAtion of cybeR Defence Incident mAnagemeNt

Published in 2023 JNIC Cybersecurity Conference (JNIC), 1-2, 2023

Artificial Intelligence and Automation are revolutionizing cyber defence and incident response by addressing traditional inefficiencies and enabling faster and easier incident resolutions. However, challenges arise in…

Recommended citation: García Cid, Marta Irene, Gil Pérez, Manuel, Jorquera Valero, José María, López Martínez, Antonio, Maestre Vidal, Jorge, Martínez Pérez, Gregorio, Méndez García, Laura, Muñoz Plaza, Frida, Nespoli, Pantaleone, Pastor Galindo, Javier, Ramón y Cajal Ramo, Pedro José, Rodríguez López, Francisco Antonio, Sánchez Sánchez, Pedro Miguel, & Sotelo Monge, Marco Antonio. (2023). "European framework and proofs-of-concept for the intelliGent aUtomAtion of cybeR Defence Incident mAnagemeNt." 2023 JNIC Cybersecurity Conference (JNIC), 1-2.
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LwHBench: A low-level hardware component benchmark and dataset for Single Board Computers

Published in Internet of Things, 22, 100764, 2023

In today’s computing environment, where Artificial Intelligence (AI) and data processing are moving toward the Internet of Things (IoT) and Edge computing paradigms, benchmarking resource-constrained devices is a…

Recommended citation: Sánchez Sánchez, Pedro Miguel, Jorquera Valero, José María, Huertas Celdrán, Alberto, Bovet, Gérôme, Gil Pérez, Manuel, & Martínez Pérez, Gregorio. (2023). "LwHBench: A low-level hardware component benchmark and dataset for Single Board Computers." Internet of Things, 22, 100764.
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Fedstellar: A Platform for Training Models in a Privacy-preserving and Decentralized Fashion

Published in International Joint Conference on Artificial Intelligence (IJCAI-23), Demo Track, 2023

Fedstellar is a platform for training decentralized, semi-decentralized, and centralized federated learning models over custom network topologies.

Recommended citation: Martínez Beltrán, Enrique Tomás, Sánchez Sánchez, Pedro Miguel, et al. (2023). "Fedstellar: A Platform for Training Models in a Privacy-preserving and Decentralized Fashion." In Proceedings of IJCAI-23, Demo Track, 7154–7157.
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Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challenges

Published in IEEE Communications Surveys & Tutorials, 25(4), 2983–3013, 2023

Survey of decentralized federated learning covering architectures, topologies, communication, security, frameworks, application scenarios, and open challenges.

Recommended citation: Martínez Beltrán, Enrique Tomás, Pérez Quiles, Mario, Sánchez Sánchez, Pedro Miguel, et al. (2023). "Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challenges." IEEE Communications Surveys & Tutorials, 25(4), 2983–3013.
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Stealth Spectrum Sensing Data Falsification Attacks Affecting IoT Spectrum Monitors on the Battlefield

Published in MILCOM 2023 - 2023 IEEE Military Communications Conference (MILCOM), 673-678, 2023

Resource-constrained spectrum sensors from the Internet of Battlefield Things (IoBT) monitor the frequency spectrum to communicate over unoccupied bands, intercept enemy transmissions, or decode valuable information.…

Recommended citation: Sánchez Sánchez, Pedro Miguel, Martínez Beltrán, Enrique Tomás, Celdrán, Alberto Huertas, Wassink, Robin, Bovet, Gérôme, Martínez Pérez, Gregorio, & Stiller, Burkhard. (2023). "Stealth Spectrum Sensing Data Falsification Attacks Affecting IoT Spectrum Monitors on the Battlefield." MILCOM 2023 - 2023 IEEE Military Communications Conference (MILCOM), 673-678.
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Trust-as-a-Service: A reputation-enabled trust framework for 5G network resource provisioning

Published in Computer Communications, 211, 229-238, 2023

Trust, security, and privacy are three of the major pillars to assemble the fifth-generation network and beyond. Despite such pillars are principally interconnected, a multitude of challenges arise that need to be…

Recommended citation: Jorquera Valero, José María, Sánchez Sánchez, Pedro Miguel, Gil Pérez, Manuel, Huertas Celdrán, Alberto, & Martínez Pérez, Gregorio. (2023). "Trust-as-a-Service: A reputation-enabled trust framework for 5G network resource provisioning." Computer Communications, 211, 229-238.
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Behavioral fingerprinting to detect ransomware in resource-constrained devices

Published in Computers & Security, 135, 103510, 2023

The Internet of Things (IoT), a network of interconnected devices, has grown and gained traction over the last few years. This paradigm can impact our lives while also providing significant economic benefits. However…

Recommended citation: Huertas Celdrán, Alberto, Sánchez Sánchez, Pedro Miguel, von der Assen, Jan, Shushack, Dennis, Perales Gómez, Ángel Luis, Bovet, Gérôme, Martínez Pérez, Gregorio, & Stiller, Burkhard. (2023). "Behavioral fingerprinting to detect ransomware in resource-constrained devices." Computers & Security, 135, 103510.
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RansomAI: AI-Powered Ransomware for Stealthy Encryption

Published in GLOBECOM 2023 - 2023 IEEE Global Communications Conference, 2578-2583, 2023

Cybersecurity solutions have shown promising performance when detecting ransomware samples that use fixed algorithms and encryption rates. However, due to the current explosion of Artificial Intelligence (AI), sooner…

Recommended citation: von der Assen, Jan, Celdrán, Alberto Huertas, Luechinger, Janik, Sánchez Sánchez, Pedro Miguel, Bovet, Gérôme, Martínez Pérez, Gregorio, & Stiller, Burkhard. (2023). "RansomAI: AI-Powered Ransomware for Stealthy Encryption." GLOBECOM 2023 - 2023 IEEE Global Communications Conference, 2578-2583.
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CyberSpec: Behavioral Fingerprinting for Intelligent Attacks Detection on Crowdsensing Spectrum Sensors

Published in IEEE Transactions on Dependable and Secure Computing, 21(1), 284-297, 2024

Integrated sensing and communication is a novel paradigm using crowdsensing spectrum sensors to help with the management of spectrum scarcity. However, well-known vulnerabilities of resource-constrained spectrum sensors…

Recommended citation: Celdrán, Alberto Huertas, Sánchez Sánchez, Pedro Miguel, Bovet, Gérôme, Martínez Pérez, Gregorio, & Stiller, Burkhard. (2024). "CyberSpec: Behavioral Fingerprinting for Intelligent Attacks Detection on Crowdsensing Spectrum Sensors." IEEE Transactions on Dependable and Secure Computing, 21(1), 284-297.
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Identification of IoT Devices Through Machine Learning and Hardware Fingerprints Based on Clock-Skew

Published in Smart Sensors, Measurement and Instrumentation, 2024

This chapter proposes a novel solution for identifying devices on a local network by analyzing differences in their internal clocks relative to a reference clock. The study consists of four stages: designing the…

Recommended citation: Sánchez Sánchez, Pedro Miguel, Celdrán, Alberto Huertas, Marín Sánchez, Sergio, Karmakar, Kallol Krishna, Bovet, Gérôme, & Pérez, Gregorio Martínez. (2024). "Identification of IoT Devices Through Machine Learning and Hardware Fingerprints Based on Clock-Skew." Smart Sensors, Measurement and Instrumentation.
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Mitigating communications threats in decentralized federated learning through moving target defense

Published in Wireless Networks, 30(9), 7407-7421, 2024

The rise of Decentralized Federated Learning (DFL) has enabled the training of machine learning models across federated participants, fostering decentralized model aggregation and reducing dependence on a server.…

Recommended citation: Martínez Beltrán, Enrique Tomás, Sánchez Sánchez, Pedro Miguel, López Bernal, Sergio, Bovet, Gérôme, Gil Pérez, Manuel, Martínez Pérez, Gregorio, & Huertas Celdrán, Alberto. (2024). "Mitigating communications threats in decentralized federated learning through moving target defense." Wireless Networks, 30(9), 7407-7421.
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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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Single-board device individual authentication based on hardware performance and autoencoder transformer models

Published in Computers & Security, 137, 103596, 2024

Individual authentication of identical IoT devices using hardware performance fingerprints and per-device Transformer autoencoders.

Recommended citation: Sánchez Sánchez, Pedro Miguel et al. (2024). "Single-board device individual authentication based on hardware performance and autoencoder transformer models." Computers & Security, 137, 103596.
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Studying the Robustness of Anti-Adversarial Federated Learning Models Detecting Cyberattacks in IoT Spectrum Sensors

Published in IEEE Transactions on Dependable and Secure Computing, 21(2), 573–584, 2024

Dataset and experimental study on the robustness of federated learning models detecting SSDF attacks in IoT spectrum sensors under data and model poisoning.

Recommended citation: Sánchez Sánchez, Pedro Miguel et al. (2024). "Studying the Robustness of Anti-Adversarial Federated Learning Models Detecting Cyberattacks in IoT Spectrum Sensors." IEEE Transactions on Dependable and Secure Computing, 21(2), 573–584.
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Analyzing the robustness of decentralized horizontal and vertical federated learning architectures in a non-IID scenario

Published in Applied Intelligence, 54(8), 6637-6653, 2024

Federated learning (FL) enables participants to collaboratively train machine and deep learning models while safeguarding data privacy. However, the FL paradigm still has drawbacks that affect its trustworthiness, as…

Recommended citation: Sánchez Sánchez, Pedro Miguel, Huertas Celdrán, Alberto, Martínez Beltrán, Enrique Tomás, Demeter, Daniel, Bovet, Gérôme, Martínez Pérez, Gregorio, & Stiller, Burkhard. (2024). "Analyzing the robustness of decentralized horizontal and vertical federated learning architectures in a non-IID scenario." Applied Intelligence, 54(8), 6637-6653.
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Fedstellar: A Platform for Decentralized Federated Learning

Published in Expert Systems with Applications, 242, 122861, 2024

In 2016, Google proposed Federated Learning (FL) as a novel paradigm to train Machine Learning (ML) models across the participants of a federation while preserving data privacy. Since its birth, Centralized FL (CFL) has…

Recommended citation: Martínez Beltrán, Enrique Tomás, Perales Gómez, Ángel Luis, Feng, Chao, Sánchez Sánchez, Pedro Miguel, López Bernal, Sergio, Bovet, Gérôme, Gil Pérez, Manuel, Martínez Pérez, Gregorio, & Huertas Celdrán, Alberto. (2024). "Fedstellar: A Platform for Decentralized Federated Learning." Expert Systems with Applications, 242, 122861.
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RL and Fingerprinting to Select Moving Target Defense Mechanisms for Zero-Day Attacks in IoT

Published in IEEE Transactions on Information Forensics and Security, 19, 5520–5529, 2024

An online reinforcement learning framework that uses behavioral fingerprinting to select Moving Target Defense techniques mitigating zero-day attacks on single-board computers.

Recommended citation: Huertas Celdrán, Alberto, Sánchez Sánchez, Pedro Miguel, et al. (2024). "RL and Fingerprinting to Select Moving Target Defense Mechanisms for Zero-Day Attacks in IoT." IEEE Transactions on Information Forensics and Security, 19, 5520–5529.
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Evaluating the impact of contextual information on the performance of intelligent continuous authentication systems

Published in Proceedings of the 19th International Conference on Availability, Reliability and Security, 1-10, 2024

Nowadays, the usage of computers ranges from activities that do not consider sensitive data, such as playing video games, to others managing confidential information, like military operations. Additionally, regardless…

Recommended citation: Sánchez Sánchez, Pedro Miguel, Abenza Cano, Adrián, Huertas Celdrán, Alberto, & Martínez Pérez, Gregorio. (2024). "Evaluating the impact of contextual information on the performance of intelligent continuous authentication systems." Proceedings of the 19th International Conference on Availability, Reliability and Security, 1-10.
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Unlocking the Potential of Knowledge Graphs: A Cyber Defense Ontology for a Knowledge Representation and Reasoning System

Published in Proceedings of the 19th International Conference on Availability, Reliability and Security, 1-9, 2024

In today’s dynamic and complex warfare landscape, characterized by the convergence of traditional and emerging threats, the significance of cybersecurity in shaping modern conflicts cannot be overstated. Such trend…

Recommended citation: Jorquera Valero, José María, López Martínez, Antonio, Sánchez Sánchez, Pedro Miguel, Navarro Martínez, Daniel, Varas López, Rodrigo, Rojo Lacal, Javier Ignacio, López Vivar, Antonio, Sotelo Monge, Marco Antonio, Gil Pérez, Manuel, & Martínez Pérez, Gregorio. (2024). "Unlocking the Potential of Knowledge Graphs: A Cyber Defense Ontology for a Knowledge Representation and Reasoning System." Proceedings of the 19th International Conference on Availability, Reliability and Security, 1-9.
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Sentinel: An Aggregation Function to Secure Decentralized Federated Learning

Published in European Conference on Artificial Intelligence (ECAI) 2024, 2024

Sentinel is a defense against poisoning attacks in decentralized federated learning, based on a three-step aggregation protocol of similarity filtering, bootstrap validation, and normalization.

Recommended citation: Feng, Chao, Huertas Celdrán, Alberto, Baltensperger, Janosch, Martínez Beltrán, Enrique Tomás, Sánchez Sánchez, Pedro Miguel, Bovet, Gérôme, & Stiller, Burkhard. (2024). "Sentinel: An Aggregation Function to Secure Decentralized Federated Learning." In ECAI 2024, Frontiers in Artificial Intelligence and Applications, 392, 1760–1767. IOS Press.
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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, 1596–1601.
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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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CyberForce: A Federated Reinforcement Learning Framework for Malware Mitigation

Published in IEEE Transactions on Dependable and Secure Computing, 22(4), 4398-4411, 2025

Recent research has shown that the integration of Reinforcement Learning (RL) with Moving Target Defense (MTD) can enhance cybersecurity in Internet-of-Things (IoT) devices. Nevertheless, the practicality of existing…

Recommended citation: Feng, Chao, Celdrán, Alberto Huertas, Sánchez Sánchez, Pedro Miguel, Kreischer, Jan, von der Assen, Jan, Bovet, Gérôme, Martínez Pérez, Gregorio, & Stiller, Burkhard. (2025). "CyberForce: A Federated Reinforcement Learning Framework for Malware Mitigation." IEEE Transactions on Dependable and Secure Computing, 22(4), 4398-4411.
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Evaluating Compact LLMs for Zero-Shot Iberian Language Tasks on End-User Devices

Published in Procesamiento del Lenguaje Natural, 75, 95–106, 2025

Evaluation of compact state-of-the-art LLMs on core NLP tasks for Iberian languages, revealing significant performance gaps for under-resourced languages such as Basque.

Recommended citation: Couto Seller, Luís, Sanz Torres, Íñigo, Vogel-Fernández, Adrián, González Carballo, Carlos, Sánchez Sánchez, Pedro Miguel, Carruana Martín, Adrián, & de Miguel Ambite, Enrique. (2025). "Evaluating Compact LLMs for Zero-Shot Iberian Language Tasks on End-User Devices." Procesamiento del Lenguaje Natural, 75, 95–106.
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Flighter: Decentralized Federated Learning and Situational Awareness for Secure Military Aerial Reconnaissance

Published in IEEE Communications Magazine, 63(10), 136–142, 2025

Flighter trains decentralized federated learning models for enemy detection in aerial reconnaissance, protecting model integrity with a situational awareness-based defense module.

Recommended citation: Martínez Beltrán, Enrique Tomás, Sánchez Sánchez, Pedro Miguel, et al. (2025). "Flighter: Decentralized Federated Learning and Situational Awareness for Secure Military Aerial Reconnaissance." IEEE Communications Magazine, 63(10), 136–142.
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TemporalFED: A Software Module for Detecting Cyberattacks in Industry 4.0 Time-Series Data Using Decentralized Federated Learning

Published in IEEE Access, 14, 64822-64835, 2026

Industry 4.0 has brought numerous advantages, such as increasing productivity through automation. However, it also presents major cybersecurity issues, such as cyberattacks affecting industrial processes. Federated…

Recommended citation: Perales Gómez, Ángel Luis, Martínez Beltrán, Enrique Tomás, Sánchez Sánchez, Pedro Miguel, & Huertas Celdrán, Alberto. (2026). "TemporalFED: A Software Module for Detecting Cyberattacks in Industry 4.0 Time-Series Data Using Decentralized Federated Learning." IEEE Access, 14, 64822-64835.
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Can’t Hide Your Stride: Inferring Car Movement Patterns from Passive TPMS Measurements

Published in 2026 21st Wireless On-Demand Network Systems and Services Conference (WONS), 77-84, 2026

Tire Pressure Monitoring System (TPMS) transmissions of modern cars are sent over the air in clear text and entail a unique identifier that does not change over very long periods of time. In this work, we investigate…

Recommended citation: Lizarribar, Yago, Scalingi, Alessio, Giustiniano, Domenico, Sánchez Sánchez, Pedro Miguel, Calvo-Palomino, Roberto, Bovet, Gérôme, & Lenders, Vincent. (2026). "Can't Hide Your Stride: Inferring Car Movement Patterns from Passive TPMS Measurements." 2026 21st Wireless On-Demand Network Systems and Services Conference (WONS), 77-84.
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