  Massive content about user's social, personal and professional life stored on
Online Social Networks (OSNs) has attracted not only the attention of
researchers and social analysts but also the cyber criminals. These cyber
criminals penetrate illegally into an OSN by establishing fake profiles or by
designing bots and exploit the vulnerabilities of an OSN to carry out illegal
activities. With the growth of technology cyber crimes have been increasing
manifold. Daily reports of the security and privacy threats in the OSNs demand
not only the intelligent automated detection systems that can identify and
alleviate fake profiles in real time but also the reinforcement of the security
and privacy laws to curtail the cyber crime. In this paper, we have studied
various categories of fake profiles like compromised profiles, cloned profiles
and online bots (spam-bots, social-bots, like-bots and influential-bots) on
different OSN sites along with existing cyber laws to mitigate their threats.
In order to design fake profile detection systems, we have highlighted
different category of fake profile features which are capable to distinguish
different kinds of fake entities from real ones. Another major challenges faced
by researchers while building the fake profile detection systems is the
unavailability of data specific to fake users. The paper addresses this
challenge by providing extremely obliging data collection techniques along with
some existing data sources. Furthermore, an attempt is made to present several
machine learning techniques employed to design different fake profile detection
systems.
