Persona:
Pastor Vargas, Rafael

Foto de perfil
Dirección de correo electrónico
ORCID
0000-0002-4089-9538
Fecha de nacimiento
Proyectos de investigación
Unidades organizativas
Puesto de trabajo
Apellidos
Pastor Vargas
Nombre de pila
Rafael
Nombre

Filtros

20252
Restablecer filtros

Ajustes

Nivel de acceso: info:eu-repo/semantics/embargoedAccess × Autor: García Merino, José Carlos ×

Resultados de la búsqueda

Mostrando 1 - 2 de 2
  • Miniatura
    Publicación
    Assessing Feature Selection Techniques for AI-based IoT Network Intrusion Detection
    (Springer, 2025-06) García Merino, José Carlos; Tobarra Abad, María de los Llanos; Robles Gómez, Antonio; Pastor Vargas, Rafael; Vidal Balboa, Pedro; Dionisio Rocha, André; Jardim Gonçalves, Ricardo
    The widespread adoption of Internet of Things (IoT) technology in rural areas has led to qualitative leaps in fields such as agriculture, livestock farming, and transportation, giving rise to the concept of Smart Rural. However, Smart Rural IoT ecosystems are often vulnerable to cyberattacks. Although Artificial Intelligence (AI) based intrusion detection systems offer an effective solution to protect these environments, IoT devices are typically constrained in terms of memory and computation capabilities, making it essential to optimise the computational burden of AI models. This work explores different feature selection techniques to develop compact and fast Random Forest models for anomaly detection in IoT environments. The obtained results demonstrate that appropriate feature selection can reduce model size and inference time by at least 45% and 8%, respectively, without compromising predictive performance.
  • Miniatura
    Publicación
    An Efficient Feature Selection Framework Using Genetic Algorithms for AI-Driven IDS in IoT Environments
    (Springer, 2025-12-01) García Merino, José Carlos; Tobarra Abad, María de los Llanos; Robles Gómez, Antonio; Pastor Vargas, Rafael; Vidal Balboa, Pedro
    The increasing concern over Internet of Things (IoT) cybersecurity, driven by the growing number of connected devices and their vulnerability to attacks, has intensified the demand for effective Intrusion Detection Systems (IDSs). In recent years Artificial Intelligence (AI) solutions has emerged as a common approach in modern cybersecurity systems, offering powerful tools for detecting complex patterns and threats. However, the limited computing capabilities of IoT environments require models that are not only accurate but also resource-efficient. Feature selection (FS) plays a key role in this context, but traditional techniques such as the Chi-2 test and mutual information focus only on predictive performance, overlooking important practical concerns like inference time and model size. In this work, we propose a novel FS approach based on a custom scoring function that combines accuracy with penalties for inference time and model size. This metric is integrated into a Genetic Algorithm framework to guide the search for optimal feature subsets. The proposed method is evaluated on the NF-ToN-IoT-v2 dataset against standard techniques. Results demonstrate that our approach achieves competitive predictive performance while significantly reducing inference time and model size, highlighting the relevance of considering deployment-oriented metrics in the FS process