  Today, Online Social Networks such as Facebook, LinkedIn and Twitter are the
most popular platforms on the Internet, on which millions of users register to
share personal information with their friends. A large amount of data, social
links and statistics about users are collected by Online Social Networks
services and they create big digital mines of various statistical data. Leakage
of personal information is a significant concern for social network users.
Besides information propagation, some new attacks on Online Social Networks
such as Identity Clone attack (ICA) have been identified. ICA attempts to
create a fake online identity of a victim to fool their friends into believing
the authenticity of the fake identity to establish social links in order to
reap the private information of the victims friends which is not shared in
their public profiles. There are some identity validation services that perform
users identity validation, but they are passive services and they only protect
users who are informed on privacy concerns and online identity issues. This
paper starts with an explanation of two types of profile cloning attacks are
explained and a new approach for detecting clone identities is proposed by
defining profile similarity and strength of relationship measures. According to
similar attributes and strength of relationship among users which are computed
in detection steps, it will be decided which profile is clone and which one is
genuine by a predetermined threshold. Finally, the experimental results are
presented to demonstrate the effectiveness of the proposed approach.
