Detecting Online Social Behaviour of Compromised Account

Abstract: Compromisation of online social network is a threat for many of us who are a part in OSN. Where many spammers establish and achieve our trust of friends and success in sending malicious spams and try to hack our account. In this paper, our goal is to analysis the social behaviour of such attackers and user, by usage of OSN services. We propose a set of social behaviouralfeatures that can effectively characterize the user social activitieson OSNs. We validate the efficacy of these behavioural features by collecting and analysing real user clickstreams to anOSN website. Based on our measurement study, we devise individual user's social behavioural profile by combining its respectivebehavioural feature metrics. A social behavioural profile accuratelyreflects a user's OSN activity patterns. While an authentic ownerconforms to its account's social behavioural profile involuntarily,it is hard and costly for impostors to feign. We evaluate thecapability of the social behavioural profiles in distinguishingdifferent OSN users, and our experimental results show thesocialbehavioural profiles can accurately differentiate individualOSN users and detect compromised accounts. Keywords: Online social behavior, privacy, data analysis,compromised accounts detection.