Programa del congreso

Energy efficiency has emerged as a cornerstone in the development of b5G/6G networks, particularly within the framework of Open Radio Access Networks (O-RAN). This paper presents a monitoring and prediction system designed to reduce the energy consumption of critical O-RAN components, namely the Central Unit (O-CU) and Distributed Unit (O-DU). The proposed approach leverages the MAPIE algorithm to generate robust energy consumption forecasts with theoretically grounded prediction intervals, allowing for real-time network resource optimization without compromising service quality. Experimental validation was conducted in a disaggregated commercial O-RAN environment, emulating realistic operational conditions, supporting the identification of consumption patterns and enabling proactive network management. The proposed system serves as a foundation for intelligent, energy-aware management strategies in future mobile networks, paving the way for automated decision-making in Service Management and Orchestration (SMO) systems.

The fifth generation of networks arrived to provide not only enhanced network capabilities but also to establish unprecedented security and privacy levels aimed at protecting end-user security and privacy. While standardization bodies have defined the required security mechanisms, there is a lack of systematic approaches for evaluating the security measures applied by 5G networks and their resiliency under fuzzing attacks. In fact, security compliance certification of any given interface is only required when there are enough offerings from the testing providers, according to TS 33.117 [2]. Currently, this approach leaves multiple critical surfaces out of the testing scope. To address this gap, we propose a complete framework built on open-source software to assess the robustness of 5G base stations in real over-the-air environments, enabling a comprehensive evaluation of security and privacy protections provided to end users.

Brain-Computer Interfaces (BCIs), are revolutionizing human-machine interactions, providing remarkable advancements across medical, industrial, educational, entertainment, and security domains. Despite BCIs' great potential, they simultaneously raise critical ethical concerns regarding user data privacy and their Neurorights, which aim to safeguard individuals against the risks of emerging neurotechnologies, especially as they become increasingly available on the market. Utilizing a comprehensive literature review and implementing a passive attack on two commercial BCI devices, the study reveals critical vulnerabilities during all the phases of the system. Our findings underscore the urgent need for robust cybersecurity frameworks, including Privacy-by-Design strategies. To address this problem, we designed a scoring framework based on the Bluetooth standard, that guides manufacturers and helps regulators, guaranteeing the safe adoption of BCIs. The proposed solution aims to enhance the security of the pairing process on BCI devices, thereby ensuring user neural data integrity and protection.

Garantizar la confianza en las redes 6G es crucial para mantener la seguridad, la fiabilidad y la idoneidad de muchas aplicaciones. La evaluación de la confianza requiere validación, integridad y protección contra manipulaciones. Este artículo explora cómo puede aprovecharse la tecnología blockchain para la evaluación de la confianza a prueba de manipulaciones en las redes 6G. Analizamos las ventajas de utilizar blockchain para una evaluación de la confianza transparente y descentralizada, destacando su resistencia a los ataques y su capacidad para garantizar la integridad de los datos. Además, abordamos los retos de aplicar blockchain en diversas aplicaciones 6G, como la utilización de recursos, la respuesta en tiempo real y la escalabilidad. El artículo también hace hincapié en el papel de los contratos inteligentes en la automatización de la gestión de la confianza y el almacenamiento seguro de la información relacionada con la confianza dentro de la blockchain, ofreciendo un enfoque innovador para mejorar la seguridad y la rendición de cuentas en las redes de próxima generación.

Traducción realizada con la versión gratuita del traductor DeepL.com

Trust and security are paramount in the emerging landscape of 6G networks, particularly as these systems become increasingly decentralized and heterogeneous. Accurate node reputation prediction is essential for maintaining robust network functionality in such environments, enabling the identification of trustworthy nodes and the mitigation of malicious behaviors. In this work, we present a novel reputation evaluation framework tailored for 6G scenarios, combining Graph Neural Networks (GNNs) for representation learning with Support Vector Machines (SVMs) for effective reputation classification. This hybrid approach captures both the structural and behavioral dynamics of network nodes, offering resilience against common threats, such as Sybil attacks and collusion. Our method addresses critical challenges in reputation prediction, including dynamic node behavior, data sparsity, and scalability across massive device deployments. Experimental results demonstrate that our GNN–SVM pipeline significantly enhances reputation prediction performance, offering a scalable and intelligent trust management solution for the next generation of 6G networks.