AI & LLMs for Cybersecurity
Large language models and intelligent recommendation systems that support threat analysis, incident triage, mitigation planning and accountable cyberdefense response.
Explore topicArtificial Intelligence · Cybersecurity · Federated Learning
Enrique Tomás Martínez Beltrán
Postdoctoral researcher at the University of Murcia working at the intersection of artificial intelligence, cybersecurity and federated learning, with a focus on threat detection, closed-loop cyberdefense, LLM-assisted analysis and trustworthy decentralized learning.
7+
Applied research years
6
EU/defense programmes
PhD
Computer Science · University of Murcia
1400+
Citations
Location
Spain
Contact
enriquetomas@um.es
Academic & Professional Profiles
Postdoctoral Researcher in Computer Science
Academic and professional profiles to follow scientific output, research identity, and technical activity.
Research Interests
Current work connects AI and federated learning with cybersecurity, cyberdefense operations, threat intelligence, IoT, edge environments and autonomous response.
Closed-loop & Autonomous Cyberdefense
Malware and cyberattack detection connected to explainable mitigation and automated response for operational, defense and military environments.
Explore topicDecentralized Federated Learning
Direct node-to-node learning for collaborative cybersecurity under adversarial behavior, heterogeneous data and communication constraints, without a single central server.
Explore topicTrustworthy & Robust AI
Robustness, explainability, accountability and reliability for AI deployed across cybersecurity, critical-infrastructure and defense environments.
Explore topicSelected Research Projects
Cybersecurity and cyberdefense projects spanning malware and cyberattack detection, federated learning and resilient AI for European defense, IoT and critical-infrastructure environments.
Apr 2023 — Nov 2023
DEFENDIS: Decentralized Federated Learning for IoT Device Identification and Security
DEFENDIS develops a framework for uniquely identifying IoT devices in a distributed manner while solving security threats through decentralized federated learning.
Security problemIoT deployments need device identity mechanisms that remain useful when central services are unavailable, compromised, or unsuitable for sensitive telemetry.
Decentralized Federated LearningAdversarial MLIoT SecurityCybersecurityTrustworthy AI
View Project
Dec 2022 — Nov 2025
EU-GUARDIAN: European Framework and Proofs-of-concept for the Intelligent Automation of Cyber Defence Incident Management
A European research project on methods and proofs of concept for supporting cyber defence incident management.
AICyberdefenseAutomation
View ProjectRecent Publications
Selected research on federated learning, cybersecurity, trustworthy AI, threat detection and secure communications, including applications to cyberdefense.
Journal article2026
Computer Networks
Asynchronous Cache-based Aggregation with Fairness and Filtering for Decentralized Federated Learning
Enrique Tomás Martínez Beltrán, Eduard Gash, Gérôme Bovet, Alberto Huertas Celdrán, Burkhard Stiller
Decentralized Federated Learning (DFL) offers a scalable paradigm for collaborative intelligence at the edge, yet its practical efficacy is severely constrained by system heterogeneity. Traditional synchronous protoco...
Journal article2026
Information Fusion
Decentralized Federated Learning with Multimodal Prototypes for Heterogeneous Data
Enrique Tomás Martínez Beltrán, Gérôme Bovet, Gregorio Martínez Pérez, Alberto Huertas Celdrán
Decentralized Federated Learning (DFL) enables collaborative machine learning across numerous devices while avoiding bottlenecks and reliance on a single trusted entity inherent to centralized architectures. However,...
Preprint2026
Submitted to Future Generation Computer Systems
Decentralized Self-Supervised Representation Learning via Prototype Exchange under Non-IID Data
Enrique Tomás Martínez Beltrán, Gérôme Bovet, Gregorio Martínez Pérez, Alberto Huertas Celdrán
Journal article2026
Future Generation Computer Systems
FedEnD: Communication-efficient Federated Learning for non-IID data via decentralized ensemble distillation
Enrique Tomás Martínez Beltrán, Philip Giryes, Gérôme Bovet, Burkhard Stiller, Gregorio Martínez Pérez, Alberto Huertas Celdrán
Federated Learning (FL) offers a paradigm for collaborative AI that mitigates raw data exposure, yet the statistical heterogeneity of client data severely constrains its practical application. This non-independent and...
Conference paper2026
XI Jornadas Nacionales de Investigación en Ciberseguridad (JNIC 2026)
MadHoney: Señuelos Tóxicos para la Defensa Activa en el Aprendizaje Federado Descentralizado
Pedro Beltrán López, Enrique Tomás Martínez Beltrán, Pantaleone Nespoli, Manuel Gil Pérez, Gregorio Martínez Pérez, Alberto Huertas Celdrán
Research Notes
Notes on federated learning, cybersecurity research, threat detection and cyberdefense workflows, including attack mitigation and LLM-assisted decision support.
May 30, 20262 months ago·8 min read
From Monitoring to Mitigation: A DFL Cyberdefense Lifecycle with LLM Explanations
A practical note on how distributed monitoring, DFL models, alert evidence and LLM-based support can fit into a cyberdefense workflow.
Decentralized Federated LearningLLMsExplainable AIAttack MitigationCyberdefense
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May 29, 20262 months ago·7 min read
Situational Awareness for Cyberdefense with Decentralized Federated Learning
A research note on using DFL to turn distributed telemetry, anomalies and trust signals into cyberdefense situational awareness.
Situational AwarenessDecentralized Federated LearningExplainable AICyberdefense
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