  Online social networks (OSNs) are abused by cyber criminals for various
malicious activities. One of the most effective approaches for detecting
malicious activity in OSNs involves the use of social network honeypots -
artificial profiles that are deliberately planted within OSNs in order to
attract abusers. Honeypot profiles have been used in detecting spammers,
potential cyber attackers, and advanced attackers. Therefore, there is a
growing need for the ability to reliably generate realistic artificial honeypot
profiles in OSNs. In this research we present 'ProfileGen' - a method for the
automated generation of profiles for professional social networks, giving
particular attention to producing realistic education and employment records.
'ProfileGen' creates honeypot profiles that are similar to actual data by
extrapolating the characteristics and properties of real data items. Evaluation
by 70 domain experts confirms the method's ability to generate realistic
artificial profiles that are indistinguishable from real profiles,
demonstrating that our method can be applied to generate realistic artificial
profiles for a wide range of applications.
