  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information of the users friends which they do not publish on the public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.
