Unveiling Hidden Threats with ML-Powered User and Entity Behavior Analytics (UEBA)

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Avinash Gupta Desetty


The ever-growing cost of cybercrime has created the need for proactive solutions for organizations seeking to protect their digital assets. While traditional security systems struggle to detect anomalies buried within vast datasets, new solutions like User and Entity Behavior Analytics (UEBA) emerge as a game-changer. By leveraging the power of machine learning, UEBA analyzes diverse data sources like user logins, file accesses, event logs, business context, external
threat intelligence, and network activity, to unveil hidden threats most traditional methods could miss. The ability to analyze multiple data sources enables UEBA solutions to effectively detect malicious insiders, compromised users, Advanced Persistent Threats (APTs), and zero-day attacks. By using various analytics techniques like supervised learning, unsupervised learning, and statistical modeling, UEBA solutions can detect subtle anomalies that deviate from
established behavior baselines. Despite the many benefits, UEBA solutions still have limitations like data quality concerns, high implementation costs, and the need for model maintenance. Integration with System Information and Event Management (SIEM) systems helps mitigate some of these challenges to further enhance UEBA's capabilities and provide a unified platform for threat identification and response. This paper provides a detailed insight into the capabilities of
UEBA, its three pillars, significance, and limitations.


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How to Cite
Desetty, A. G. . (2024). Unveiling Hidden Threats with ML-Powered User and Entity Behavior Analytics (UEBA). Turkish Journal of Computer and Mathematics Education (TURCOMAT), 15(1), 44–50. https://doi.org/10.61841/turcomat.v15i1.14394


Statista, “Estimated cost of cybercrime worldwide 2017-2028.’ Available online:


Wade W., Barbara F., “The Expanding Role of Data Analytics in Threat Detection,” October 2015.

Michael R., “What is the difference between signature-based and behavior-based intrusion detection systems?”

December 2020. Available online: https://accedian.com/blog/what-is-the-difference-between-signature-based-andbehavior-based-ids/

Timothy J., Shimeall, Jonathan M., Spring, “Introduction to Information Security,” 2014. Available online:


IBM, “What is UEBA (user and entity behavior analytics)?” Available Online:


Gartner, “Market Guide for User and Entity Behavior Analytics,” May 2019. Available online:


Jason C., Jay B., “UEBA: Canary in a Coal Mine,” April 2017. Available online:


Splunk, “4 Reasons to Add UBA to Your SIEM.” Available online: https://www.splunk.com/en_us/form/4-


GateWatcher, “ Benefits of a UEBA Approach.” Available online:


Aujas, “How to Mitigate Insider Threats with SIEM & UEBA,” July 2020. Available online:


Oskar C., | Daniel N., “User and Entity Behavior Anomaly Detection using Network Traffic,” 2017. Available

online: https://www.diva-portal.org/smash/get/diva2:1113229/FULLTEXT02

Derek L., “Applying data science to user and entity behavior analytics,” 2016. Available online:


Exabeam, “What Is UEBA (User and Entity Behavior Analytics)?” Available online:


Linan H., Quanyan Z., “A dynamic games approach to proactive defense strategies against Advanced

Persistent Threats in cyber-physical systems,” 2020. Available online: