Listar Telemática por autor "Fernández, Diego"

Mostrando ítems 1-12 de 12

    • A Content-Based Approach to Profile Expansion 

      Fernández, Diego; Formoso, Vreixo; Cacheda, Fidel; Carneiro, Víctor (World Scientific, 2020-12)
      [Abstract]: Collaborative Filtering algorithms suffer from the so-called cold-start problem. In particular, when a user has rated few items, recommendations offered by these algorithms are not too accurate. Profile Expansion ...

    • 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 ...

    • 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 ...

    • 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, ...

    • Early Intrusion Detection for OS Scan Attacks 

      López-Vizcaíno, Manuel F.; Nóvoa, Francisco; Fernández, Diego; Carneiro, Víctor; Cacheda, Fidel (Institute of Electrical and Electronics Engineers Inc., 2019-09)
      [Abstract]: Network Intrusion Detection Systems (NIDS) are concerned with the discovery of unauthorized accesses to computer networks by analyzing the traffic in order to detect malicious activity. In the event of an ...

    • 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, Carlos; 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 ...

    • Time Aware F-Score for Cybersecurity Early Detection Evaluation 

      López-Vizcaíno, Manuel F.; Novoa, Francisco; Fernández, Diego; Cacheda, Fidel (MDPI, 2024-01)
      [Abstract]: With the increase in the use of Internet interconnected systems, security has become of utmost importance. One key element to guarantee an adequate level of security is being able to detect the threat as soon ...