# What is User and Entity Behavior Analytics? Behavior-Based Defense Against Cyber Threats

As cyber attacks become increasingly sophisticated today, traditional security solutions are proving insufficient in many situations. While firewalls, antivirus software, and intrusion detection systems are effective against known threats, they can be limited against zero-day attacks, insider threats, and advanced persistent threats (APT). This is precisely where User and Entity Behavior Analytics (UEBA) comes into play, creating a paradigm shift in cybersecurity strategies.

User and Entity Behavior Analytics is an advanced cybersecurity technology that detects anomalies by analyzing the normal behavior patterns of users and entities. UEBA solutions continuously monitor events occurring on corporate networks using machine learning and artificial intelligence algorithms, identify deviations from normal behaviors, and proactively detect potential security threats.

Core Components of UEBA Technology

User and Entity Behavior Analytics systems are formed by the combination of multiple technological components. Each of these components plays a critical role in the overall effectiveness of the system.

Data Collection and Aggregation

The first step of UEBA solutions is collecting data from various sources in the corporate environment. These sources include log files, network traffic, authentication systems, database access records, endpoint security systems, and cloud applications. The collected data is consolidated on a central platform and prepared for analysis.

Machine Learning and Artificial Intelligence

The heart of UEBA is machine learning algorithms. These algorithms analyze collected data to create normal behavior profiles for each user and entity. Using supervised and unsupervised learning methods, the system becomes smarter over time and can detect anomalies more accurately.

Behavior Profiling

A unique behavior profile is created for each user. This profile includes what hours the user works, which systems they access, how much data they download, which geographic locations they connect from, and many other parameters. Similarly, behavior profiles are created for entities such as servers, applications, and IoT devices.

How UEBA Differs from Traditional Security Solutions

Traditional security systems typically use rule-based approaches. They attempt to detect threats according to specific signatures or predefined rules. However, this approach is ineffective against previously unseen attacks or attacks carried out with legitimate credentials.

UEBA adopts a behavior-based approach. By detecting deviations from normal behavior, it can identify even attack types that have never been seen before. For example, if an employee always connects from the office during business hours, a connection attempt from a different country at midnight is immediately detected.

Risk Scoring and Prioritization

UEBA systems calculate a risk score for each detected anomaly. This score takes into account the severity of the anomaly, the user's access privileges, the sensitivity of affected assets, and other contextual factors. Security teams can thus prioritize the most critical threats and reduce alarm fatigue.

UEBA Use Cases

Insider Threat Detection

Insider threats are among the most dangerous and difficult-to-detect security risks for organizations. A malicious employee or a legitimate user whose credentials have been compromised can easily bypass traditional security systems. UEBA detects behaviors such as an employee suddenly attempting to access files they don't have authorization for, downloading unusual amounts of data, or trying to send sensitive information to external sources.

Account Takeover Attacks

Cybercriminals can gain access to corporate accounts using stolen credentials. UEBA detects accounts performing activities different from the legitimate user's normal behavior patterns. For example, connecting to systems not normally accessed from an administrator's account or logging in at unusual hours immediately generates an alert.

Data Leakage and Exfiltration

Unauthorized external transfer of corporate data is one of the areas where UEBA is most effective. The system learns a user's normal data transfer patterns and detects suspicious activities such as abnormally large file transfers, compressed file submissions, or data transfer over encrypted channels.

Advanced Persistent Threats (APT)

APT attacks are sophisticated attacks that can remain undetected in systems for long periods. UEBA can detect the subtle behavioral traces left by such attacks. APT indicators such as lateral movement, privilege escalation, and command-and-control communication can be revealed through behavior analysis.

Success Factors in UEBA Implementation

Data Quality and Scope

The effectiveness of UEBA is directly related to the quality and scope of the data fed to it. Integrating as many data sources as possible provides more accurate behavior profiles and better anomaly detection. Log management, SIEM systems, endpoint detection and response (EDR) solutions, and cloud security tools should be integrated with UEBA.

Learning Period and Fine-Tuning

UEBA systems require a learning period to function effectively. Typically within a 30-90 day period, the system learns normal behavior patterns. During this time, false positive rates may be high. Security teams need to regularly review and optimize the system's settings.

Security Team Training

While UEBA is a powerful tool, it requires well-trained security analysts for effective use. Teams need to know how to interpret anomaly scores, how to reduce false positives, and how to respond to detected threats.