Combating the evolving spammers in online social networks. Issue 72 (January 2018)

• Record Type: Journal Article
• Authors: Fu, Qiang; Feng, Bo; Guo, Dong; Li, Qiang
• Is Part Of: Computers & security. Issue 72(2018)
• Page Start: 60
• Page End: 73
• Publication Date: 2018-01
• Subjects: Online social networks -- Spammer detection -- Temporal evolution -- Machine learning -- Classification; Computer security -- Periodicals; Electronic data processing departments -- Security measures -- Periodicals; 005.805
• DOI: 10.1016/j.cose.2017.08.014
• Languages: English
• ISSNs: 0167-4048

Abstract

Highlights: A dynamic metric model to measure the changes of users' activities is proposed. We tracked users' activities for three months to build our dataset. We design new features to describe the temporal evolution patterns of users. A detecting framework combining unsupervised clustering and supervised classification. Our approach achieves better performance than conventional detection methods. Abstract: Online social networks, such as Facebook and Sina Weibo, have become the most popular platforms for information sharing and social activities. Spammers have utilized social networks as a new way to spread spam information using fake accounts. Many detection methods have been proposed to solve this problem, and have been proved to be successful to some extent. However, as the spammers' strategies for evading detection evolve, many existing methods lose their efficacy. A major limitation of previous approaches is that they are using the features from a static time point to detect spammers, without considering temporal factors. In this study, we approach the challenge of spammer detection by leveraging the temporal evolution patterns of users. We propose a dynamic metric to measure the change in users' activities and design new features to quantify users' evolution patterns. Then we develop a framework by combining unsupervised and supervised learning to distinguish between spammers and legitimate users. We test our method on a real world dataset with a large number of users. The evaluation results show that our approach can efficiently distinguish the difference between spammers and legitimate users regarding temporal evolution patterns. It also demonstrates the high level of similarity in the spammers' temporal evolution patterns. Compared with other detection methods, our method can achieve better performance. To the best of our knowledge, our study is the first to provide a generic and efficient framework to depict the evolutional pattern of users. It can handle the problem of spammers updating their strategies to evade detection and is a valuable reference for this research field.