A novel machine learning approach to the detection of identity theft in social networks based on emulated attack instances and support vector machines. (20th September 2015)

• Record Type: Journal Article
• Authors: Villar‐Rodríguez, E.; Del Ser, J.; Torre‐Bastida, A. I.; Bilbao, M. N.; Salcedo‐Sanz, S.
• Other Names: Simmhan Yogesh guestEditor.; Ramakrishnan Lavanya guestEditor.; Antoniu Gabriel guestEditor.; Goble Carole guestEditor.; Yu Yong guestEditor.; Mu Yi guestEditor.; Lu Rongxing guestEditor.; Ren Jian guestEditor.; Venticinque Salvatore guestEditor.; Camacho David guestEditor.
• Is Part Of: Concurrency and computation. Volume 28:Number 4(2016)
• Page Start: 1385
• Page End: 1395
• Publication Date: 2015-09-20
• Subjects: identity theft -- social networks -- machine learning -- support vector machines; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.3633
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: 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. Copyright © 2015 John Wiley & Sons, Ltd.