Main Article Content
Abstract
In the present generation, the social life of everyone has become associated with the online social networks (OSN) like Facebook, Twitter etc. These sites have made a drastic change in the way we pursue our social life. Making friends and keeping in contact with them and their updates has become easier. But with the rapid growth in their membership profiles, many problems like fake profiles, online impersonation have also grown. It is almost impossible to examine such huge profiles manually to control these problems. In literature, there are no feasible solution exist to control these problems automatically. In this paper, we propose a framework for automatic detection of fake profiles by the OSNs. This framework will use classification techniques like Support Vector Machine, Nave Bayes Probabilistic and Decision trees approach to best classify the profiles into fake or genuine classes. The simulation results prove the efficiency of the proposed framework.