Paper Title
A Review of User Entity Behavior Analytics in The Field of Cybersecurity

Abstract
This review paper explores the realm of User and Entity Behavior Analytics (UEBA) within the cybersecurity landscape. User Entity Behavior Analytics, an innovative approach leveraging advanced analytics and machine learning, focuses on monitoring and analyzing patterns of user and entity activities to identify anomalies and potential security vulnerabilities. This mechanism uses the statistical techniques and machine learning algorithms to draw patterns and then detect the anomalies to throw alert based on the severity of the attack. In order to shed light on UEBA's efficacy and possible areas for progress, this study presents a thorough analysis of current breakthroughs, difficulties, and trends in the field and it also talks about the benefits of integrating UEBA with System Information Event Management (SIEM) to highlight how the two technologies work in concert with one another. A review of User Entity Behavior Analytics design and approach is discussed in this paper to limit the cyber attacks. Keywords - Cybersecurity, User Entity Behavior Analytics, Machine Learning, Anomaly Detection