Behavioral Analysis for Identity Theft Detection in Online Social Networks

This study presents a method of detecting identity theft in online social networks using composite behavioral modeling. The suggested model analyzes user behavior patterns and looks for anomalies that could be signs of identity theft using machine learning techniques like Random Forest, Gradient Boosting Machines (GBM), and Neural Networks. To identify suspicious activity, the algorithm takes into account variables including the frequency of logins, modifications to friend networks, and posting patterns of content. According to the study, composite behavioral modeling greatly raises detection accuracy and offers an effective approach to improving user safety and cybersecurity in social networks.