Detecting clone profiles in social media networks

Abstract

With the popularity of Online Social Networks (OSN), the number of different types of digital attacks has been increased causing lots of damages to their users. Identity Clone Attack (ICA) is one of the leading among them which illegally uses the information of a genuine user by duplicating them in another fake profile. These attacks severely affect a true and innocent identity since it can be misused by another malicious profile. Hence these clone profiles need to be identified and removed in order to increase the protection of users. Many researchers have tried to solve the problem of clone profiles in OSN, however more robust solutions are still to be taken. This study introduces a model to detect clone profiles on Facebook by clustering based on weighted categorical attributes and estimating the strength of friend relationship among friends. The list of possible clones with the amount of clone percentages to a given victim profile was presented as the output of the model. With the use of Agglomerative hierarchical clustering algorithm and Jaccard similarity measurement, a low average within cluster distance and a precision of 88.75% has shown in the results