UEBA and Behavioral Analytics for Insider Threats
Apply user and entity behavior analytics to detect anomalous behavior patterns that indicate insider threats or compromised credentials.
What Is UEBA?
User and Entity Behavior Analytics (UEBA) is a security analytics approach that builds behavioral baselines for users and entities (endpoints, servers, applications) and uses statistical models and machine learning to detect anomalous behavior. Unlike rule-based detection, UEBA can catch threats that have no known signature — attackers using legitimate tools, insiders abusing access, and compromised accounts operating within policy but outside normal behavior patterns.
Baseline Building: What Is Normal?
UEBA effectiveness depends on accurate behavioral baselines. For users, baseline attributes include: typical login hours, usual source locations, devices regularly used, typical data access volumes, and frequent application usage. For entities, baselines include: normal network traffic patterns, standard service accounts' access scope, and regular process behaviors. UEBA systems typically observe the environment for 2-4 weeks before producing reliable anomaly scores.
# User baseline attributes example:
# User: jsmith@company.com
# Normal login: Mon-Fri 08:00-18:00 EST
# Typical location: New York office / Home VPN
# Devices: Laptop-001, iPhone-022
# Avg daily file accesses: 45
# Avg outbound data: 12MB/day
# Applications: Salesforce, Office365, Slack
# ANOMALY triggers:
# - Login at 03:00 from Berlin IP
# - 4.2GB file download in single sessionAll lessons in this course
- Threat Hunting Methodology and Hypothesis Generation
- SIEM Architecture: Log Ingestion, Parsing, and Correlation
- Writing SIEM Detection Rules and Alerts
- UEBA and Behavioral Analytics for Insider Threats