User and Entity Behavior Analytics (UEBA)
A cybersecurity technique that analyzes user and entity behavior to detect anomalies.
Understanding User and Entity Behavior Analytics (UEBA)
User and Entity Behavior Analytics (UEBA) is a cybersecurity approach that leverages machine learning, artificial intelligence, and statistical analysis to detect abnormal behavior patterns in users and entities (such as applications, devices, and networks). Unlike traditional security systems that rely on predefined rules, UEBA helps identify insider threats, compromised accounts, and advanced persistent threats by analyzing behavior trends.
Key Components of UEBA
User Behavior Analysis
Monitors user activities such as login attempts, file access, and privilege escalations.
Detects deviations from normal behavior patterns.
Entity Behavior Analysis
Observes machine, application, and network activities to detect anomalies.
Identifies potential threats originating from non-user entities.
Threat Detection Algorithms
Uses AI and machine learning to differentiate between normal and suspicious activities.
Detects credential misuse, lateral movement, and privilege abuse.
Challenges and Considerations
Data Privacy Concerns – Monitoring user behavior may raise privacy issues.
Integration Complexity – Requires seamless integration with existing security tools like SIEM.
High Data Processing Requirements – UEBA systems analyze large volumes of data in real-time.
Behavioral False Positives – Requires fine-tuning to distinguish genuine user behavior changes from malicious activity.