User and Entity Behavior Analytics (UEBA)
What Is UEBA?
User and Entity Behavior Analytics (UEBA) is a new category of security solutions that use machine learning and deep learning algorithms to analyze the typical behavior of users and entities on corporate networks. By understanding normal activity patterns, UEBA identifies and flags abnormal behavior that could pose security risks, such as zero-day attacks and insider threats. Unlike traditional security tools relying on correlation rules and known attack patterns, UEBA uncovers emerging threats that would otherwise go undetected.
Core Capabilities of UEBA
- Behavioral Profiling: By creating comprehensive profiles for each user and entity, UEBA establishes a baseline of normal activities, enabling highly accurate anomaly detection.
- Anomaly Detection: Advanced analytics and machine learning algorithms identify deviations from established behavior patterns, providing early warning of potential threats.
- Contextual Analysis: Incorporates contextual information from diverse data sources to accurately distinguish between genuine threats and false alarms.
- Risk Scoring: Assigns individual risk scores to detected events, helping prioritize incidents that need immediate attention based on potential impact.
Benefits of UEBA
Early Detection of Insider Threats
UEBA can detect changes in behavior that may indicate a malicious insider, reducing the risk of data leaks and fraud.
Rapid Identification of Compromised Accounts
Identifies attackers using stolen credentials and tracks lateral movements as they attempt to penetrate deeper into IT systems.
Effective Incident Prioritization
Predicts which incidents are particularly dangerous or suspicious by adding context about the affected assets' criticality.
Alert Management and Data Loss Prevention (DLP)
Prioritizes and consolidates alerts, reducing alert fatigue and helping security teams quickly identify genuine data leaks.
Key Differences Between UEBA and SIEM
- Scope: SIEM systems focus on collecting and organizing security data across an organization to support incident response and compliance processes. UEBA, while sharing similar data sources, focuses on behavioral analysis and anomaly detection.
- Analysis Approach: SIEM solutions traditionally rely on correlation rules and predefined attack patterns. In contrast, UEBA leverages machine learning to uncover new and emerging threats hiding in normal behavior patterns.
- Integration: UEBA enhances the analytical capabilities of SIEMs by providing sophisticated anomaly detection and behavioral analysis, complementing traditional security monitoring.
Use Cases for UEBA
- Malicious Insider Detection: UEBA identifies employees exhibiting suspicious behavior that deviates from their typical activity patterns, flagging potential insider threats.
- Compromised Account Detection: Detects suspicious activities performed by attackers using compromised accounts and monitors lateral movements.
- Incident Prioritization: Identifies incidents that are particularly dangerous by leveraging advanced behavioral analytics to pinpoint abnormal activities.
- Entity Analytics (IoT): Tracks thousands of IoT devices and establishes baselines to detect malicious activities like unusual device connections or functions.