Listar por autor "Fernández, Diego"

Mostrando ítems 1-13 de 13

    • Annotated Dataset for Anomaly Detection in a Data Center with IoT Sensors 

      Vigoya, Laura; Fernández, Diego; Carneiro, Víctor; Cacheda, Fidel (MDPI AG, 2020-07-04)
      [Abstract] The relative simplicity of IoT networks extends service vulnerabilities and possibilities to different network failures exhibiting system weaknesses. Therefore, having a dataset with a sufficient number of ...

    • Anomaly Detection in IoT: Methods, Techniques and Tools 

      Vigoya, Laura; López-Vizcaíno, Manuel F.; Fernández, Diego; Carneiro, Víctor (MDPI AG, 2019-07-22)
      [Abstract] Nowadays, the Internet of things (IoT) network, as system of interrelated computing devices with the ability to transfer data over a network, is present in many scenarios of everyday life. Understanding how ...

    • Application of machine learning algorithms for the validation of a new CoAP-IoT anomaly detection dataset 

      Vigoya, Laura; Pardal Noya, Alberto; Fernández, Diego; Carneiro, Víctor (MDPI, 2023-04)
      [Abstract]: With the rise in smart devices, the Internet of Things (IoT) has been established as one of the preferred emerging platforms to fulfil their need for simple interconnections. The use of specific protocols such ...

    • Un concurso de cortos para el refuerzo pedagógico y la mejora de la participación del alumnado 

      Fernández, Diego; Cacheda, Fidel; Nóvoa, Francisco; Carneiro, Víctor (2018)
      [Resumen] En la asignatura de Redes del Grado en Ingeniería Informática de la Universidade da Coruña se explican los fundamentos de la comunicación a través de una red de computadores. Para incentivar la participación ...

    • Early Detection of Depression: Social Network Analysis and Random Forest Techniques 

      Cacheda, Fidel; Fernández, Diego; Nóvoa, Francisco; Carneiro, Víctor (J M I R Publications, Inc., 2019-06-10)
      [Abstract] Background: Major depressive disorder (MDD) or depression is among the most prevalent psychiatric disorders, affecting more than 300 million people globally. Early detection is critical for rapid intervention, ...

    • High Order Profile Expansion to tackle the new user problem on recommender systems 

      Fernández, Diego; Formoso, Vreixo; Cacheda, Fidel; Carneiro, Víctor (Public Library of Science, 2019-11-07)
      [Abstract] Collaborative Filtering algorithms provide users with recommendations based on their opinions, that is, on the ratings given by the user for some items. They are the most popular and widely implemented algorithms ...

    • IoT Dataset Validation Using Machine Learning Techniques for Traffic Anomaly Detection 

      Vigoya, Laura; Fernández, Diego; Carneiro, Víctor; Nóvoa, Francisco (MDPI, 2021)
      [Abstract] With advancements in engineering and science, the application of smart systems is increasing, generating a faster growth of the IoT network traffic. The limitations due to IoT restricted power and computing ...

    • Low Cost Automated Security Audit System 

      Fernández-Arruti Gallego, Pedro; Estévez Pereira, Julio Jairo; Nóvoa, Francisco; Dafonte, Carlos; Fernández, Diego (MDPI, 2021)
      [Abstract] In recent years, a quick transition towards digitization has been observed in most organizations. Along with it, certain inherent problems have appeared, such as the increase in cyber threats. Large organizations ...

    • Measuring Early Detection of Anomalies 

      López-Vizcaíno, Manuel F.; Novoa, Francisco; Fernández, Diego; Cacheda, Fidel (IEEE, 2022)
      [Abstract] Early detection is a matter of growing importance in multiple domains as network security, health conditions over social network services or weather forecasts related disasters. It is not enough to make a good ...

    • Network Anomaly Detection Using Machine Learning Techniques 

      Estévez Pereira, Julio Jairo; Fernández, Diego; Nóvoa, Francisco (MDPI AG, 2020-08-19)
      [Abstract] While traditional network security methods have been proven useful until now, the flexibility of machine learning techniques makes them a solid candidate in the current scene of our networks. In this paper, we ...

    • Network Data Flow Clustering based on Unsupervised Learning 

      López-Vizcaíno, Manuel F.; Dafonte, C.; Nóvoa, Francisco; Garabato, D.; Álvarez, M. A.; Fernández, Diego (Institute of Electrical and Electronics Engineers Inc., 2019)
      [Abstract]: Network communication data analysis is crucial in order to provide an adequate security level in computer infrastructures. As the volume of data and the number of features rise, the difficulties associated with ...

    • Use of Machine Learning Algorithms for Network Traffic Classification 

      Nieto Antelo, Adrián; Fernández, Diego; Nóvoa, Francisco (Universidade da Coruña, Servizo de Publicacións, 2023)
      [Abstract] In recent years, the complexity of threats utilizing the network as an attack vector has significantly increased. Traditional attack prevention and detection systems (IPS/IDS) based on signatures do not provide ...

    • Uso de técnicas de recomendación en sistemas dispersos 

      Fernández, Diego (2014)
      [Resumen] En esta tesis nos centramos en el problema que padecen los sistemas de recomendación basados en filtrado colaborativo para recomendar cuando pocos usuarios han valorado los mismos productos: el problema de la ...