Enhancing Security in Social Networks through ML: Detecting and Mitigating Sybil Attacks

This study contributes to the Sybil nodes detecting algorithm in Online Social Networks (OSNs). As major communication platforms, online social networks get significantly guarded from malicious activity. A thorough literature review identified various detection and prevention Sybil attack algorithms. Additional exploration of distinct reputation systems and their practical application led to the studys discovery of machine learning algorithms, i.e., the KNN, Support Vector Machines, and Random Forest algorithms. The study details the data cleansing process for the employed dataset in its process for optimizing the computational demands required to train machine learning algorithms, achieved through dataset partitioning. Such a process led to explaining and analysis of conducting experiments and comparing their results. Such experiments demonstrate the algorithms ability to detect Sybil nodes in OSNs (100% accuracy in SVM, 99.6% in Random Forest, and 97% in KNN algorithms); thus concluding by proposing future research opportunities.