SybilTrap: A graph‐based semi‐supervised Sybil defense scheme for online social networks. (11th September 2017)

• Record Type: Journal Article
• Authors: Al‐Qurishi, Muhammad; Rahman, Sk Md Mizanur; Alamri, Atif; Mostafa, Mohamed A.; Al‐Rubaian, Majed; Hossain, M. Shamim; Gupta, B.B.
• Other Names: Kutylowski Miroslaw guestEditor.; Wang Yu guestEditor.; Xu Shouhuai guestEditor.; Yang Laurence T. guestEditor.
• Is Part Of: Concurrency and computation. Volume 30:Number 5(2018)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2017-09-11
• Subjects: social network -- Sybil attack -- Sybil defense -- targeted attack; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.4276
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: Sybil attacks are increasingly prevalent in online social networks. A malicious user can generate a huge number of fake accounts to produce spam, impersonate other users, commit fraud, and reach many legitimate users. For security reasons, such fake accounts have to be detected and deactivated immediately. Various defense schemes have been proposed to deal with fake accounts. However, most identify fake accounts using only the structure of social graphs, leading to poor performance. In this paper, we propose a new and scalable defense scheme, SybilTrap. SybilTrap uses a semi‐supervised technique that automatically integrates the underlying features of user activities with the social structure into one system. Unlike other machine learning–based approaches, the proposed defense scheme works on unlabeled data, and it is effective in detecting targeted attacks, because it manipulates different levels of features of user profiles. We evaluate SybilTrap on a dataset collected from Twitter. We show that our proposed scheme is able to accurately detect Sybil nodes as well as huge conspiracies among them.