Security analysis of the data of social networks using AI techniques

Abstract

This proposed research explores the potential of AI techniques, particularly user engagement prediction, for analyzing social network data and identifying potential security threats. Utilizing a Random Forest classifier, we developed a highly accurate model achieving 100% accuracy and a 1.0 AUC-ROC score. This exceptional performance demonstrates the ability of engagement prediction to accurately flag suspicious accounts with unusually low engagement, often associated with bots or fake profiles. Based on these findings, we implemented mitigation strategies such as flagging low-engagement accounts for further investigation and analyzing engagement trends to inform proactive security measures. Furthermore, our work opens doors for future research in combining engagement prediction with other AI techniques like sentiment analysis for even more sophisticated threat detection, ultimately contributing to the development of robust solutions for enhanced social network security and user privacy protection.