User & Entity Behavior Analytics (UEBA), Explained

User and Entity Behavior Analytics, or UBEA, uses large datasets to model typical and atypical behaviors of humans and machines within a network. This means that UEBA can detect non-malware-based attacks because it analyzes various behavioral patterns. UEBA also uses these models to assess the threat level, creating a risk score that can help guide the appropriate response. Increasingly, UEBA uses machine learning to identify normal behavior and alert to anomalies and risky deviations that suggest insider threats, lateral movement, compromised accounts, and attacks.

Baselining is key to a UEBA system, as it makes it possible to detect potential threats. The UEBA system compares the established baseline with current user behavior, calculates a risk score, and determines if deviations are acceptable. If the risk score exceeds a certain threshold, the system alerts security analysts in real-time. By defining such baselines, UEBA can identify suspicious behavior, potential threats, and attacks that traditional antivirus may not detect.

The 3 Pillars of UEBA

  1. Use cases: UEBA solutions report the behavior of entities and users in a network, to detect, monitor, and alert on anomalies. UEBA solutions need to be relevant and support  multiple use cases.

    Gartner sees UEBA being applied to use cases where finer-tuned analytics and gathering more context is essential, including:

    – Malicious Insiders
    – APT groups leveraging zero-day vulnerabilities
    – Data exfiltration involving novel channels
    – User Account access monitoring
  1. Data sources: UEBA solutions can ingest data from a general data repository. Such repositories include:

    – Data warehouse
    – Data lake
    – Security Information and Event Management (SIEM)
  2. Analytics: UEBA solutions isolate anomalies using analytic methods, including machine learning, statistical models, rules, and threat signatures.


Security information and event management (SIEM) is the use of a complex set of tools and technologies that give organizations a comprehensive view of their IT security system. It makes use of data and event information, allowing visibility into normal patterns, and delivering alerts when there are unusual circumstances and events. SIEM is similar to UEBA in the sense of using user and entity behavior information to define what is considered a normal behavior and what is not. 

SIEMs are good security management tools but are less sophisticated when it comes to more advanced threat detection and response. SIEMs can handle real-time threats rather easily, but they may be unable to detect sophisticated cyberattacks. This is because sophisticated cyberattacks avoid simple one-off threats and instead engage in an extended attack that can go undetected by traditional threat management tools for several weeks or even months.

On the other hand, UEBA solutions can detect more sophisticated threats, such as those that might be undetectable today but over time display a surprising pattern. Malvertising is an example of this, a seemingly harmless advertising applet downloaded to a browser that collects user data or infects a user’s device.

By stacking UEBA and SIEM tools together, enterprises are better able to defend themselves against a wide range of threats. By focusing less on system events and more on specific user or entity activities, UEBA builds a profile of an employee or entity based on usage patterns and sends out an alert if it sees unusual or suspicious user behavior. 

UEBA Benefits

With UEBA suspicious user behavior can be detected in the cloud, on-premises, and inside business applications – with an unparalleled time-to-value. Other benefits include:

  • The primary pro of UEBA is that it allows you to automatically detect a wide range of cyber-attacks
  • Because UEBA allows fewer security analysts to do more, it can also significantly reduce your cybersecurity budget
  • UEBA can drastically reduce the detection time of malware outbreaks by using algorithm-driven analytics to detect beaconing, lateral movement, or weaponization
  • Outputs from the UEBA module can be correlated with SIEM events, making the original events more insightful than ever
  • Discover suspicious user behavior by statically or dynamically enriching the original log data using the information from machine learning
  • Incidents can be visualized using dashboards and search templates for faster threat hunting


With CYBOWALL you don’t have to settle for either as it offers both SIEM and UEBA technologies. In addition to those, CYBOWALL offers 7 other security engines that help protects the organization from ongoing threats and cover:

  • Asset Management
  • Vulnerability Assessment
  • Honeypot
  • File Integrity Monitor (FIM)
  • Intrusion Detection (IDS)
  • Malicious Traffic Detection
  • Malware Hunter

The above security solutions are orchestrated by UEBA as well, and by a powerful Attack HunterTM; an Autonomous Machine Learning and Multi-step Attack Hunting Engine. Together, they allow organizations to manage all security threats from one unified platform, offering more protection than any other solution in the market.

To learn more and to book a demo click here.

Written by: Moshe Amiel, Cyber & AI Expert, Advisory Board Member at CYBOWALL