  In the recent years, the fast development and the exponential utilization of
social networks have prompted an expansion of social Computing. In social
networks users are interconnected by edges or links, where Facebook, twitter,
LinkedIn are most popular social networks websites. Due to the growing
popularity of these sites they serve as a target for cyber criminality and
attacks. It is mostly based on how users are using these sites like Twitter and
others.
  Attackers can easily access and gather personal and sensitive users
information. Users are less aware and least concerned about the security
setting. And they easily become victim of identity breach. To detect malicious
users or fake profiles different techniques have been proposed like our
approach which is based on the use of social honeypots to discover malicious
profiles in it. Inspired by security researchers who used honeypots to observe
and analyze malicious activity in the networks, this method uses social
honeypots to trap malicious users. The two key elements of the approach are:
(1) The deployment of social honeypots for harvesting information of malicious
profiles. (2) Analysis of the characteristics of these malicious profiles and
those of deployed honeypots for creating classifiers that allow to filter the
existing profiles and monitor the new profiles.
