Martín Herranz Sánchez, Álvaro Llorente, Alberto del Rio and David Jimenez

Professional live video production has traditionally been limited by proprietary hardware mixers and monolithic desktop applications, which impose prohibitive costs and restrict scalable, command-line-based implementation for remote integration (REMI) workflows. To address these limitations, this article presents Voctomix 2.0, a containerized and enhanced live video mixing architecture based on the open-source Voctomix framework. This research introduces a production-ready extension layer that adds dynamic multi-layer overlays, operator-level stream-blanking control with coupled audio muting, and an asynchronous AMQP telemetry framework on top of the upstream mixing engine. The architecture was empirically evaluated in local, Docker, and single-node Kubernetes environments under varying workloads, ranging from 1080p25 to 2160p50 (4K). Results under a continuous four-source 1080p25 workload showed that, in a single-node deployment, containerization and orchestration impose no appreciable processing overhead, with a median host-level CPU utilization near 90% and a stable RAM working set of around 13 GB on standard hardware. The median command-to-output latency within the mixer was 293 ms, an internal switching latency that does not include camera capture or wide-area transport. Forced-failure experiments validated the architecture’s self-healing capability, which autonomously recovered blocked camera signals in approximately 1.8 s while maintaining a continuous live program feed.

Costas Bampos, Vasileios Megalooikonomou

Graphical representations model complex networks by encoding entities as vertices and interactions as edges, with recurring subgraphs—or motifs—revealing fundamental organizational principles. We present a novel application of Hidden Markov Models (HMMs) to network motif detection: subgraphs are encoded as short symbolic sequences and scored with standard HMM kernels (Viterbi/Forward; optional Baum–Welch), producing graded likelihoods that tolerate missing or noisy edges. On a 253-node directed benchmark the HMM pipeline recovers known 4-node motifs with accuracy comparable to exact enumeration while providing a probabilistic, weight-aware scoring framework that enables principled model comparison. We also include a concise complexity comparison with ESU, FANMOD and G-Tries and discuss engineering choices (seed-and-filter, scoring-only workflows) that make the approach practical. To our knowledge, this is the first application of HMMs to network motif detection.

Theodore Zahariadis, Artemis Voulkidis, Ilias Seitanidis, Andreas Papadakis, Alberto del Rio, Javier Serrano, David Jiménez, Antonio Pastor, Diego R. López, Alejandro Muñiz, Mattin A. Elorza Forcada, Ana Méndez, Wafa Ben Jaballah, Rosaria Rossini, Maria Belesioti, Ioannis Chochliouros, Marco Angelini, Vasileios Megalooikonomou, Carmela Occhipinti, Luigi Briguglio, Alexandru Plesa, Vladut Dinu, Mohammad Ghoreishi, Mostafa Jabari, Dimitrios Skias, Konstantinos Sakatis, Ioannis Papaefstathiou

According to the EU State of Cybersecurity report by the European Union Agency for Cybersecurity (ENISA), the number of cybersecurity-related incidents will increase by 24 percent by 2025, with ransomware and DoS/DDoS attacks being the most common. The emergence of new threats [1] and the consolidation of existing ones require doubling of efforts in proactive prevention and a decisive increase in research dedicated to cybersecurity. CyberNEMO (End-to-end Cybersecurity to NEMO meta-OS) project emerges as an evolution of the NEMO (Next Generation Meta Operating System) platform, designed to provide a secure, trustworthy, and robust execution environment across the IoT-Edge-Cloud computing continuum. Leveraging NEMO modular meta-operating system (mOS) framework, CyberNEMO introduces advanced cybersecurity and privacy-preserving mechanisms, emphasizing Zero Trust principles. This paper presents the CyberNEMO architecture, details its core innovative technologies, and describes its validation strategy through diverse living labs-including Smart Energy, Smart Water, Smart Manufacturing, Healthcare, Multimedia Distribution, and Smart Farming scenarios-demonstrating end-to-end cybersecurity and real-time threat mitigation capabilities, aligned with Europe’s strategic cybersecurity goals.

Sofía Ortiz-Arce, Álvaro Llorente, Alberto del Rio, Federico Alvarez

Evaluating Quality of Experience (QoE) is crucial for multimedia services, as it measures user satisfaction with content delivery. This paper presents an objective method for evaluating QoE in adaptive streaming services, using the commercial tool Video-MOS, which allows real-time monitoring and analysis of multimedia content quality across various platforms and networks. The method aims to provide a precise evaluation that incorporates both technical and subjective factors. This approach integrates multiple factors that influence the overall user perception of adaptive streaming quality, offering greater flexibility and performance, which could contribute to more comprehensive future assessment. The method is validated by a test plan that incorporates a variety of content and scenarios to simulate various network conditions. The results demonstrate the method’s effectiveness in predicting QoE, highlighting the rebuffering frequency as a significant factor. In optimal conditions, specific content types can achieve a QoE score as high as 3.36. Conversely, under unfavorable conditions, the QoE may decrease by up to 1.42 Mean Opinion Score (MOS) points, which represents an 80% reduction from its optimal level. Although the rebuffering frequency has a substantial influence, a long initial buffer can have an even more negative effect on QoE, particularly under adverse conditions. Furthermore, adaptive streaming technologies, such as Dynamic Adaptive Streaming over HTTP (MPEG-DASH) and Adaptive Bitrate Streaming (ABR) are integral to the assessment process.

Ana Pérez Méndez, Lucía Cabanillas, Diego López

Ensuring data integrity and traceability poses a significant challenge in contemporary network environments, particularly in the context of cloud-native architectures, Software- Defined Networks (SDN), Internet of Things (IoT), and imminent 6G technologies. Conventional data collection and verification methodologies have deficiencies in terms of transparency and auditability, limiting decision-making processes and their automation. To address these issues, we propose the application of a Transparent Notary Service (TNS), a digital notarization framework that enables the secure registration and assessment of evidence statements on network elements, using append-only logs and cryptographic signatures. The proposed system leverages efficient digital signatures and verifiable and immutable records to provide a trusted evidence registry, enabling entities to register signed statements while ensuring their integrity without the need for content validation. This approach enhances Zero Trust Architectures (ZTA) by providing cryptographically verifiable data that can support access control decisions, network orchestration, and forensic auditing. This paper presents a conceptual framework and an initial design approach for the TNS, outlining its key components and potential implementation paths rather than a fully deployed system. The proposed framework ensures secure, efficient, and transparent evidence registration, offering a lightweight alternative to traditional blockchain-based solutions. Future work will explore extended format support, enhanced storage optimization, and automated integrity verification mechanisms.

Pilar Schummer, Alberto del Rio, Javier Serrano, David Jimenez, Guillermo Sánchez, Álvaro Llorente

In the last decade, numerous methods have been proposed to define and detect outliers, particularly in complex environments like networks, where anomalies significantly deviate from normal patterns. Although defining a clear standard is challenging, anomaly detection systems have become essential for network administrators to efficiently identify and resolve irregularities. Methods: This study develops and evaluates a machine learning-based system for network anomaly detection, focusing on point anomalies within network traffic. It employs both unsupervised and supervised learning techniques, including change point detection, clustering, and classification models, to identify anomalies. SHAP values are utilized to enhance model interpretability. Results: Unsupervised models effectively captured temporal patterns, while supervised models, particularly Random Forest (94.3%), demonstrated high accuracy in classifying anomalies, closely approximating the actual anomaly rate. Conclusions: Experimental results indicate that the system can accurately predict network anomalies in advance. Congestion and packet loss were identified as key factors in anomaly detection. This study demonstrates the potential for real-world deployment of the anomaly detection system to validate its scalability.