Enrique Tomás Martínez Beltrán | AI & Cybersecurity Research

Research. Protect. Collaborate.

Postdoctoral Researcher in Computer Science

University of Murcia

AI, cybersecurity and federated learning

I research at the intersection of artificial intelligence, cybersecurity and federated learning to build systems that detect threats, protect data and learn in a decentralized, trustworthy way.

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AI & LLMs for CybersecurityLarge language models and intelligent recommendation systems that support threat analysis, incident triage, mitigation planning and accountable cyberdefense response.
Closed-loop & Autonomous CyberdefenseMalware and cyberattack detection connected to explainable mitigation and automated response for operational, defense and military environments.
Decentralized Federated LearningDirect node-to-node learning for collaborative cybersecurity under adversarial behavior, heterogeneous data and communication constraints, without a single central server.

Applied research years: 7+
Computer Science / University of Murcia: PhD
Google Scholar citations: 1700+
EU and defence programmes: 6

Academic & Professional Profiles

Find publications, research identity and technical work across the main academic profiles.

AffiliationCyberDataLabopens in a new tab / University of Murciaopens in a new tab

Recent Publications

Recent work on federated learning, cybersecurity and trustworthy AI, with a short note on why each contribution matters.

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SYNAPSE: Framework for neuron analysis and perturbation in sequence encoding

Journal article / Applied Soft Computing / 2027

SYNAPSE analyzes Transformer neurons and stress-tests internal robustness through controlled interventions during inference, without retraining the base model.

Asynchronous Cache-based Aggregation with Fairness and Filtering for Decentralized Federated Learning

Journal article / Computer Networks / 2026

CAFF addresses delays and unequal participation in asynchronous DFL. A cache with one slot per neighbour, staleness filtering and an adaptive threshold balance training efficiency and predictive quality.

Decentralized Federated Learning with Multimodal Prototypes for Heterogeneous Data

Journal article / Information Fusion / 2026

Modalis addresses clients with different or missing modalities in decentralized learning. It exchanges compact class prototypes and aligns local representations to support collaboration across heterogeneous data.

Selected Research Projects

Three projects linking distributed AI, cyberdefense and trustworthy security research across European and applied settings.

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ECYSAP EYE / Dec 2024 / Jan 2028

ECYSAP EYE: European Cyber Situational Awareness Platform - Enhanced Cyberspace Operations

An architectural evolution of the European cyber situational awareness platform into a modular System of Systems to support military missions.

Cybersecurity
Cyberdefense
Situational Awareness

ROBUST-6G / Jan 2024 / Jun 2026

ROBUST-6G: Smart, Automated and Reliable Security Service Platform for 6G

ROBUST-6G studies security mechanisms for 6G systems, including monitoring, secure data management, trustworthy AI services, federated learning, and threat response.

Cybersecurity
Cyberdefense
Trustworthy AI

DEFENDIS / 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.

Decentralized Federated Learning
Adversarial ML
IoT Security

Research Notes

Selected research notes and technical guides connecting federated learning, threat detection and cyberdefense workflows. The archive includes every note in publication order.

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