Profiling Online Social Behaviors for Compromised Account Detection

• Published in: IEEE transactions on information forensics and security Vol. 11; no. 1; pp. 176 - 187
• Main Authors: Xin Ruan, Zhenyu Wu, Haining Wang, Jajodia, Sushil
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Behavior; Browsers; Computer information security; Effectiveness; Facebook; Feature extraction; Forensic engineering; Information dissemination; Measurement; Navigation; Online; Profiling; Social networks; Spamming; Uniform resource locators; Online social behavior; privacy; data analysis; compromised accounts detection
• ISSN: 1556-6013; 1556-6021
• DOI: 10.1109/TIFS.2015.2482465

Account compromization is a serious threat to users of online social networks (OSNs). While relentless spammers exploit the established trust relationships between account owners and their friends to efficiently spread malicious spam, timely detection of compromised accounts is quite challenging due to the well established trust relationship between the service providers, account owners, and their friends. In this paper, we study the social behaviors of OSN users, i.e., their usage of OSN services, and the application of which in detecting the compromised accounts. In particular, we propose a set of social behavioral features that can effectively characterize the user social activities on OSNs. We validate the efficacy of these behavioral features by collecting and analyzing real user clickstreams to an OSN website. Based on our measurement study, we devise individual user's social behavioral profile by combining its respective behavioral feature metrics. A social behavioral profile accurately reflects a user's OSN activity patterns. While an authentic owner conforms to its account's social behavioral profile involuntarily, it is hard and costly for impostors to feign. We evaluate the capability of the social behavioral profiles in distinguishing different OSN users, and our experimental results show the social behavioral profiles can accurately differentiate individual OSN users and detect compromised accounts.