Examinando por Autor "Vidal Balboa, Pedro"

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
    Privacy Analysis in Mobile Apps and Social Networks Using AI Techniques
    (IEEE - Institute of Electrical and Electronics Engineers, 2024-09-17) Blanco Aza, Daniel; Robles Gómez, Antonio; Pastor Vargas, Rafael; Tobarra Abad, María de los Llanos; Vidal Balboa, Pedro; Méndez Suárez, Mariano
    In the current landscape of mobile applications and social networks, privacy concerns have become paramount due to the extensive collection and processing of personal data. Therefore, this paper presents a comprehensive review of the state-of-the-art on automated privacy risk analysis in mobile applications and social networks. This review includes various methodologies, tools and frameworks that use ML and NLP systems to assess and ensure compliance with privacy regulations, such as the GDPR. Through a careful application of the PRISMA methodology, key studies have been systematically analyzed. Our findings reveal significant progress in the integration of automated techniques for assessing privacy risks.