Browsing by Keyword "Identity theft"

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    Gazela: asesor digital de redes sociales para adolescentes
    (2012-09-01) Isasi-Andrieu, Alberto; López-Carrera, Ångel; Ruiz-Ibáñez, Pilar; Angulo-Redondo, Iñaki; Tecnalia Research & Innovation; CIBERSEC&DLT; SG; SG; DIGITAL ENERGY
    Social networks are becoming overcrowded. Every day people share millions of files, videos and pictures, and the massive use of social networks is replacing other communications channels such as telephone, e-mail or sms. However, these networks are often used without knowing the possible repercussions. Gazela is a platform with the aim to provide the general society and especially teenagers with the necessary tools to allow them to guarantee their privacy while using social networks. Gazela's activity is focused on three main elements: surveillance services, protection services and legal advising services.
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    A novel machine learning approach to the detection of identity theft in social networks based on emulated attack instances and support vector machines
    (2016-03-25) Villar-Rodríguez, E.; Del Ser, Javier; Torre-Bastida, A. I.; Bilbao, M. N.; Salcedo-Sanz, Sancho; Quantum; IA; HPA
    The proliferation of social networks and their usage by a wide spectrum of user profiles has been specially notable in the last decade. A social network is frequently conceived as a strongly interlinked community of users, each featuring a compact neighborhood tightly and actively connected through different communication flows. This realm unleashes a rich substrate for a myriad of malicious activities aimed at unauthorizedly profiting from the user itself or from his/her social circle. This manuscript elaborates on a practical approach for the detection of identity theft in social networks, by which the credentials of a certain user are stolen and used without permission by the attacker for its own benefit. The proposed scheme detects identity thefts by exclusively analyzing connection time traces of the account being tested in a nonintrusive manner. The manuscript formulates the detection of this attack as a binary classification problem, which is tackled by means of a support vector classifier applied over features inferred from the original connection time traces of the user. Simulation results are discussed in depth toward elucidating the potentiality of the proposed system as the first step of a more involved impersonation detection framework, also relying on connectivity patterns and elements from language processing.