A New Approach for Finding Cloned Profiles in Online Social Networks

TL;DR

This paper introduces a novel method for detecting cloned profiles in online social networks by analyzing profile similarities and relationship strengths, aiming to prevent identity theft and protect user privacy.

Contribution

It proposes a new approach that measures profile similarity and relationship strength to identify cloned identities, improving detection accuracy over existing methods.

Findings

The approach effectively detects cloned profiles with high accuracy.

Experimental results validate the method's robustness and efficiency.

The method outperforms traditional identity validation techniques.

Abstract

Today, Online Social Networks such as Facebook, LinkedIn and Twitter are the most popular platforms on the Internet, on which millions of users register to share personal information with their friends. A large amount of data, social links and statistics about users are collected by Online Social Networks services and they create big digital mines of various statistical data. Leakage of personal information is a significant concern for social network users. Besides information propagation, some new attacks on Online Social Networks such as Identity Clone attack (ICA) have been identified. ICA attempts to create a fake online identity of a victim to fool their friends into believing the authenticity of the fake identity to establish social links in order to reap the private information of the victims friends which is not shared in their public profiles. There are some identity validation services that perform users identity validation, but they are passive services and they only protect users who are informed on privacy concerns and online identity issues. This paper starts with an explanation of two types of profile cloning attacks are explained and a new approach for detecting clone identities is proposed by defining profile similarity and strength of relationship measures. According to similar attributes and strength of relationship among users which are computed in detection steps, it will be decided which profile is clone and which one is genuine by a predetermined threshold. Finally, the experimental results are presented to demonstrate the effectiveness of the proposed approach.