Now showing items 1-5 of 5

    • Nuevos Paradigmas de Análisis Basados en Contenidos para la Detección del Spam en RRSS 

      Ezpeleta Gallastegi, Enaitz (Sociedad Española para el Procesamiento del Lenguaje Natural, 2018)
      Tesis doctoral realizada por Enaitz Ezpeleta Gallastegi en Mondragon Unibertsitatea, dentro del grupo de Sistemas Inteligentes para Sistemas Industriales, dirigida por los Doctores Urko Zurutuza Ortega (Mondragon Unibertsitatea) ...
    • Short Messages Spam Filtering Combining Personality Recognition and Sentiment Analysis 

      Ezpeleta Gallastegi, Enaitz; Garitano Garitano, Iñaki; Zurutuza Ortega, Urko (World Scientific Publishing, 2017)
      Currently, short communication channels are growing up due to the huge increase in the number of smartphones and online social networks users. This growth attracts malicious campaigns, such as spam campaigns, that are a ...
    • Short Messages Spam Filtering Using Sentiment Analysis 

      Ezpeleta Gallastegi, Enaitz; Zurutuza Ortega, Urko (Springer International Publishing, 2016)
      In the same way that short instant messages are more and more used, spam and non-legitimate campaigns through this type of communication systems are growing up. Those campaigns, besides being an illegal online activity, ...
    • Software Defined Networking Opportunities for Intelligent Security Enhancement of Industrial Control Systems 

      Sainz Oruna, Mikel; Iturbe Urretxa, Mikel; Garitano Garitano, Iñaki; Zurutuza Ortega, Urko (Springer International Publishing, 2017)
      In the last years, cyber security of Industrial Control Systems (ICSs) has become an important issue due to the discovery of sophisticated malware that by attacking Critical Infrastructures, could cause catastrophic safety ...
    • A study of the personalization of spam content using Facebook public information 

      Ezpeleta Gallastegi, Enaitz; Zurutuza Ortega, Urko (Oxford University Press, 2017)
      Millions of users per day are affected by unsolicited email campaigns. Spam filters are capable of detecting and avoiding an increasing number of messages, but researchers have quantified a response rate of a 0.006% [1], ...