Abigail Paradise, Dvir Cohen, Asaf Shabtai, Rami Puzis
2018 IEEE/ACM International Conference on Advances in Social Networks …, 2018
One of the most effective approaches for detecting malicious activity in online social networks (OSNs) involves the use of social network honeypots – artificial profiles. 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.