Abstract

User and Entity Behavior Analytics (UEBA) has emerged as a crucial approach in modern cybersecurity for detecting and mitigating insider threats, compromised accounts, and other malicious activities within organizational networks. However, existing UEBA frameworks often face challenges in scalability, detection accuracy, and response effectiveness. This research work proposes a novel framework for UEBA that aims to address these limitations and enhance threat detection and response capabilities. The framework integrates advanced machine learning algorithms, behavioral analytics techniques, and threat intelligence to establish baseline behaviors, detect anomalies, and prioritize response actions. Key components of the framework include user and entity profiling, behavioral analytics, risk scoring, and incident detection and response mechanisms. In user and entity profiling, comprehensive profiles are created for both users and entities (e.g., devices, applications) within the network, capturing relevant attributes and historical behaviors. Behavioral analytics leverages these profiles to identify deviations from normal behavior patterns, signaling potential security incidents. Risk scoring assigns severity levels to detected anomalies based on their potential impact and likelihood, enabling prioritization of response efforts. Overall, this research contributes to advancing the field of UEBA by providing a comprehensive framework that addresses scalability, accuracy, and effectiveness challenges. It lays the groundwork for developing more robust and adaptive cybersecurity solutions to combat evolving threats effectively, ensuring the security and integrity of organizational networks in an increasingly complex threat landscape.

Keyworde: UEBA, cybersecurity, threat detection, security framework, security analysis, behavioral analytics, threat intelligence

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