Fake profile detection techniques in large-scale online social networks: A comprehensive review. (January 2018)

• Record Type: Journal Article
• Authors: Ramalingam, Devakunchari; Chinnaiah, Valliyammai
• Is Part Of: Computers & electrical engineering. Volume 65(2018)
• Page Start: 165
• Page End: 177
• Publication Date: 2018-01
• Subjects: Fake profile detection -- Online social networks -- Sybil attacks -- Big data; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2017.05.020
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: We aim to provide a review of existing and state-of-the art Sybil detection methods with an introductory approach. We focus on various approaches and algorithms of Sybil detection and compared the approaches. We present some of the emerging open issues for Sybil detection in Online Social Networks. Abstract: In the present era, online social networks are the most popular and rapid information propagation applications on the Internet. People of all ages spend most of their time on social networking sites. Huge volumes of data are being created and shared through social networks around the world. These interests have given rise to illegitimate users who engage in fraudulent activities against social network users. On social networks, fake profile creation is considered to cause more harm than any other form of cyber crime. This crime has to be detected even before the user is notified about the fake profile creation. Many algorithms and methods, most of which use the huge volume of unstructured data generated from social networks, have been proposed for the detection of fake profiles. This study presents a survey of the existing and latest technical work on fake profile detection. Graphical abstract: