An IAC Approach for Detecting Profile Cloning in Online Social Networks

Nowadays, Online Social Networks are popular websites on the internet, which millions of users register on and share their own personal information with others. Privacy threats and disclosing personal information are the most important concerns of OSNs users. Recently, a new attack which is named Identity Cloned Attack is detected on OSNs. In this attack the attacker tries to make a fake identity of a real user in order to access to private information of the users friends which they do not publish on the public profiles. In today OSNs, there are some verification services, but they are not active services and they are useful for users who are familiar with online identity issues. In this paper, Identity cloned attacks are explained in more details and a new and precise method to detect profile cloning in online social networks is proposed. In this method, first, the social network is shown in a form of graph, then, according to similarities among users, this graph is divided into smaller communities. Afterwards, all of the similar profiles to the real profile are gathered (from the same community), then strength of relationship (among all selected profiles and the real profile) is calculated, and those which have the less strength of relationship will be verified by mutual friend system. In this study, in order to evaluate the effectiveness of proposed method, all steps are applied on a dataset of Facebook, and finally this work is compared with two previous works by applying them on the dataset.