J
José Gaviria de la Puerta
Researcher at University of Deusto
Publications - 23
Citations - 377
José Gaviria de la Puerta is an academic researcher from University of Deusto. The author has contributed to research in topics: Malware & Botnet. The author has an hindex of 7, co-authored 21 publications receiving 302 citations.
Papers
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Proceedings Article
Supervised Machine Learning for the Detection of Troll Profiles in Twitter Social Network: Application to a Real Case of Cyberbullying.
Patxi Galán-García,José Gaviria de la Puerta,Carlos Laorden Gómez,Igor Santos,Pablo García Bringas +4 more
TL;DR: A methodology to detect and associate fake profiles on Twitter social network which are employed for defamatory activities to a real profile within the same network by analysing the content of comments generated by both profiles is presented.
Journal ArticleDOI
Supervised machine learning for the detection of troll profiles in twitter social network: application to a real case of cyberbullying
Patxi Galán-García,José Gaviria de la Puerta,Carlos Laorden Gómez,Igor Santos,Pablo García Bringas +4 more
TL;DR: In this article, the authors present a methodology to detect and associate fake profiles on Twitter social network which are employed for defamatory activities to a real profile within the same network by analyzing the content of comments generated by both profiles.
Book ChapterDOI
Using Dalvik Opcodes for Malware Detection on Android
TL;DR: An approach to detect malware on Android is presented, by using the techniques of reverse engineering and putting an emphasis on operational codes used for these applications.
Journal ArticleDOI
Territorial innovation models: to be or not to be, that’s the question
David Doloreux,José Gaviria de la Puerta,Iker Pastor-López,Igone Porto Gómez,Borja Sanz,Jon Mikel Zabala-Iturriagagoitia +5 more
TL;DR: If there are clear boundaries across TIMs, so each TIM has particular characteristics that make it conceptually different from others, and hence, justify its introduction in the literature, is clarified.
Journal ArticleDOI
Using Dalvik opcodes for malware detection on android
TL;DR: An approach to detect malware on Android is presented, by using the techniques of reverse engineering and putting an emphasis on operational codes used for these applications.