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Sernández Iglesias, Daniel

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  • Miniatura
    Publicación
    Plataforma IoT para el Análisis e Investigación de la Ciberseguridad en Entornos Rurales Inteligentes
    (Universidad Nacional de Educación a Distancia (UNED). E.T.S. de Ingeniería Informática, 2025-06) Sernández Iglesias, Daniel; Robles Gómez, Antonio; Pastor Vargas, Rafael
    Las áreas rurales se enfrentan a importantes barreras para la adopción de tecnologías IoT debido a conectividad limitada, restricciones energéticas y una infraestructura técnica deficiente. Mientras que los entornos urbanos se benefician de sistemas digitales avanzados y servicios en la nube, las áreas rurales a menudo carecen de las condiciones necesarias para desplegar y evaluar soluciones IoT seguras y autónomas. Con el objetivo de contribuir a superar esta brecha, este trabajo presenta el Smart Rural IoT Lab, un banco de pruebas modular y reproducible diseñado para replicar las condiciones de despliegue de sistemas IoT en zonas rurales mediante el uso de herramientas de código abierto y hardware comercialmente asequible. El laboratorio integra tecnologías de comunicación de corto y largo alcance en seis escenarios experimentales, implementando protocolos como MQTT, HTTP, UDP y CoAP. Estos escenarios simulan casos de uso rurales realistas, entre ellos la monitorización ambiental, el seguimiento de ganado, el control de acceso a infraestructuras y la protección de bienes patrimoniales. El procesamiento local de datos se realiza a través de servicios desplegados en contenedores Docker, como Node-RED, InfluxDB, MongoDB y Grafana, lo que garantiza una autonomía completa sin necesidad de recurrir a servicios en la nube. Una de las principales aportaciones del laboratorio es la generación de conjuntos de datos estructurados a partir del tráfico real de red capturado con Tcpdump y preprocesado con Zeek. A diferencia de los conjuntos de datos simulados, los datos recogidos reflejan patrones de comunicación generados por dispositivos reales. Aunque el conjunto actual solo incluye tráfico benigno, la plataforma está preparada para la futura incorporación de escenarios adversos (como suplantación de identidad o ataques de denegación de servicio) que respalden investigaciones en ciberseguridad basada en inteligencia artificial. Si bien los experimentos se llevaron a cabo en un entorno controlado en interiores, la arquitectura del banco de pruebas es portátil y adecuada para futuras implementaciones en exteriores. El Smart Rural IoT Lab responde así a una necesidad crítica en la infraestructura de investigación actual, proporcionando una base realista y flexible para el desarrollo de soluciones IoT seguras e independientes de la nube, contribuyendo a la transformación digital de las regiones rurales.
  • Miniatura
    Publicación
    Automated IoT vulnerability classification using Deep Learning
    (2025-07-01) Enrique Fernández Morales,; García Merino, José Carlos; Tobarra Abad, María de los Llanos; Pastor Vargas, Rafael; Robles Gómez, Antonio; Sarraipa, Joao; Sernández Iglesias, Daniel
    Technological advancements in the development of low-power chips have enabled everyday objects to connect to the Internet, giving rise to the concept known as the Internet of Things (IoT). It is currently estimated that there are approximately 16 billion IoT connections worldwide, a figure expected to double by 2030. However, this rapid growth of the IoT ecosystem has introduced new vulnerabilities that could be exploited by malicious actors. Since many IoT devices handle personal and sensitive information, threats to these devices can have severe consequences. Moreover, a series of cybersecurity incidents could undermine public trust in IoT technology, potentially delaying its widespread adoption across various sectors.Common Vulnerabilities and Exposures records (also known by their acronym as CVEs) is a public cataloging system designed to identify and list known security vulnerabilities in software and hardware products. This system is developed and maintained by MITRE with the support of the cybersecurity community and sponsored by the U.S. Department of Homeland Security (DHS) through the Cybersecurity and Infrastructure Security Agency (CISA). CVE provides a reference database that enables security researchers, manufacturers, and organizational security managers to more effectively identify and address security issues.In our study, we have focused on CVEs exclusively oriented towards IoT systems, with the aim of analyzing the main vulnerabilities detected from 2010 to nowadays as a basis for detecting the main attack vectors in IoT systems. As part of this effort we have created the following dataset. CVEs records include various metrics such as: - Common Weakness Enumeration (CWE), mainly focused on technical classification of vulnerabilities. - Common Vulnerability Scoring System (CVSS), which reports about different metrics such as the attack vector, the severity of the vulnerability or the impact level of the exploitation of the vulnerability. This is one of the most informative metric. - Stakeholder-Specific Vulnerability Categorization (SSVC), oriented towards help cybersecurity team to handle properly the vulnerability. These metrics allow security teams on the one hand to prioritize, such vulnerabilities within their security program, evaluating efforts to mitigate them. But according to our analysis of our dataset, around the 14% of CVEs records do not contain any metric. Around the 83% of CVEs registries contain CWE metric (an ID or its textual description). This metric, as it is explained before, only reports about the type of vulnerability from a technic point of view. Only the 10% of CVEs registries contain SSVC metrics. And CVSS, in its different versions, appears only in the 40% of the studied CVEs registries. Additionally, most of studied records includes metrics a retrospectively, several weeks or months later the vulnerability is disclosed. Thus, cybersecurity teams must trust their previous knowledge in order to distinguish which vulnerabilities are relevant and which not.To tackled this situation, our proposal is focused in the application of Deep Learning techniques in order to classify the severity of CVE records from its textual description. Textual description is a mandatory field that is present in all CVEs records. To achieve this objective, we trained the BiLSTM algorithm using the CVE records with CVSS metrics and its description field; and performed a comparative study of different hyperparameter configurations to find the optimal configuration. The metrics for model evaluation that have been studied are accuracy, loss and F1-score.
  • Miniatura
    Publicación
    Internet of Things Platform for Assessment and Research on Cybersecurity of Smart Rural Environments
    (MDPI, 2025-08-01) Sernández Iglesias, Daniel; Tobarra Abad, María de los Llanos; Pastor Vargas, Rafael; Robles Gómez, Antonio; Vidal Balboa, Pedro; Sarraipa, João; Instituto Nacional de Ciberseguridad (INCIBE); (NextGenerationEU/PRTR) Unión Europea
    Rural regions face significant barriers to adopting IoT technologies, due to limited connectivity, energy constraints, and poor technical infrastructure. While urban environments benefit from advanced digital systems and cloud services, rural areas often lack the necessary conditions to deploy and evaluate secure and autonomous IoT solutions. To help overcome this gap, this paper presents the Smart Rural IoT Lab, a modular and reproducible testbed designed to replicate the deployment conditions in rural areas using open-source tools and affordable hardware. The laboratory integrates long-range and short-range communication technologies in six experimental scenarios, implementing protocols such as MQTT, HTTP, UDP, and CoAP. These scenarios simulate realistic rural use cases, including environmental monitoring, livestock tracking, infrastructure access control, and heritage site protection. Local data processing is achieved through containerized services like Node-RED, InfluxDB, MongoDB, and Grafana, ensuring complete autonomy, without dependence on cloud services. A key contribution of the laboratory is the generation of structured datasets from real network traffic captured with Tcpdump and preprocessed using Zeek. Unlike simulated datasets, the collected data reflect communication patterns generated from real devices. Although the current dataset only includes benign traffic, the platform is prepared for future incorporation of adversarial scenarios (spoofing, DoS) to support AI-based cybersecurity research. While experiments were conducted in an indoor controlled environment, the testbed architecture is portable and suitable for future outdoor deployment. The Smart Rural IoT Lab addresses a critical gap in current research infrastructure, providing a realistic and flexible foundation for developing secure, cloud-independent IoT solutions, contributing to the digital transformation of rural regions.