Mostrando ítems 26-30 de 31

    • 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 ...
    • 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 ...
    • Early Detection of Cyberbullying on Social Media Networks 

      López-Vizcaíno, Manuel F.; Nóvoa, Francisco; Carneiro, Víctor; Cacheda, Fidel (Elsevier BV, 2021-05)
      [Abstract] Cyberbullying is an important issue for our society and has a major negative effect on the victims, that can be highly damaging due to the frequency and high propagation provided by Information Technologies. ...
    • 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 ...
    • 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 ...