A novel machine learning approach to the detection of identity theft in social networks based on emulated attack instances and support vector machines
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Author/sVillar-Rodríguez, E.; Del Ser, Javier; Torre-Bastida, A. I.; Bilbao, M. N.; Salcedo-Sanz, Sancho
Support vector machines
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 ...