Increasing the Accuracy of Sibyl Attack Detection in Social Networks using Hybrid Clustering Method based on a Graph-structure

Sybil attacks are increasingly growing and expanding in social networks. A malicious user with a fake identity, known as a Sybil attack, can create a large number of fake accounts to generate spam, impersonate other users, commit fraud, and gain access to many legitimate users' information. For security reasons, such fake accounts should be identified and disabled. Various identification methods have been proposed to deal with fake accounts. However, most of these methods detect fake accounts using social structure graphs, which leads to poor performance, or use machine learning methods, which have low accuracy for identifying Sybil attacks. In this paper, a hybrid clustering method called CRNM is proposed. The proposed method is based on clustering, so that by combining different community detection methods; A new community detection method is presented. The combination of these methods has led to higher accuracy, more reliable results and more stability. The CRNM method has been evaluated on datasets collected from Twitter, Reddit, Instagram and Facebook. Unlike other machine learning-based approaches, the proposed method focuses on different levels of user profile features. The evaluation results have shown that the CRNM method detects Sybil nodes with an accuracy of 85.13%.