User Behavior Analytics in Advanced Persistent Threats: A Comprehensive Review of Detection and Mitigation Strategies

Advanced Persistent Threats (APTs) represent one of the most formidable challenges in modern cyber security. These highly advanced and persistent attacks are designed to infiltrate and remain undetected within targeted systems for extended periods. In response to this evolving threat landscape, User Behavior Analytics (UBA) has emerged as a promising approach to enhance APT detection and mitigation. This paper presents a comprehensive review of UBA methodologies and their application to combating APTs. This study explores the fundamental principles of UBA, the integration of machine learning algorithms, and the utilization of behavioral analysis to identify suspicious user activities. Additionally, this paper analyzes real-world case studies to highlight the benefits and challenges of implementing UBA in APT detection and proposes effective mitigation strategies.