Persona:
Pastor Vargas, Rafael

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0000-0002-4089-9538
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Pastor Vargas
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Rafael
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20253
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Nivel de acceso: info:eu-repo/semantics/embargoedAccess × Autor: Vidal Balboa, Pedro ×

Resultados de la búsqueda

Mostrando 1 - 3 de 3
  • Miniatura
    Publicación
    Anomaly Detection in Smart Rural IoT Systems
    (Springer, 2025-06) Fernández Morales, Enrique; Tobarra Abad, María de los Llanos; Robles Gómez, Antonio; Pastor Vargas, Rafael; Vidal Balboa, Pedro; Sarraipa, Joao
    The main contribution of this work is based on the presentation of new AI models for the detection of attacks, within IoT systems using an extensive and complete dataset. In this context, we evaluate not only the performance of the models in terms of detection of attacks, but also their resource consumption, such as the time needed to analyze a sample, the consumption of computing cycles to analyze a sample, as well as the hard disk usage to store the AI models. Its application is oriented to the context of IoT systems in rural environments, where devices deployed in these environments usually have strong restrictions on these resources. Our results indicate that the OPTIMIST-LSTM model offers the best balance between accuracy and generalization, whereas XAI-IoT stands out for its computational efficiency, making them the most suitable for implementation in IoT infrastructures with limited resources.
  • 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