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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2016-03-25
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Abstract
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.
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Publisher Copyright: Copyright © 2015 John Wiley & Sons, Ltd.
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Identity theft , Social networks , Machine learning , Support vector machines , Identity theft , Social networks , Machine learning , Support vector machines , Software , Theoretical Computer Science , Computer Science Applications , Computer Networks and Communications , Computational Theory and Mathematics , Funding Info , The presented work has been possible thanks to the funding support of the Basque Government_x000D_ under the CYBERSID project grant and the computing infrastructure of the i2BASQUE_x000D_ academic network. , The presented work has been possible thanks to the funding support of the Basque Government_x000D_ under the CYBERSID project grant and the computing infrastructure of the i2BASQUE_x000D_ academic network.
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Villar-RodrÃguez , E , Del Ser , J , Torre-Bastida , A I , Bilbao , M N & Salcedo-Sanz , S 2016 , ' A novel machine learning approach to the detection of identity theft in social networks based on emulated attack instances and support vector machines ' , Concurrency and Computation: Practice and Experience , vol. 28 , no. 4 , pp. 1385-1395 . https://doi.org/10.1002/cpe.3633