Application of User and Entity Behavioral Analytics (UEBA) in the Detection of Cyber Threats and Vulnerabilities Management

Research summary

The summary, key findings, methodology and relevance notes below are this website’s own description of the paper, written from the published abstract and text. The official abstract and citation details are given further down.

The Problem

Organizations that depend on open networks and cloud services face a growing volume of cyber threats that rule-based defences struggle to detect.

The Methodology

Conceptual and applied overview (short conference paper) of AI-based user and entity behavioural analytics for cyber threat detection and vulnerability management; no empirical dataset is reported in the abstract.

The Core Finding

The paper positions user and entity behavioural analytics (UEBA) as an AI approach that scans large volumes of system and network data to discover where attacks originate and to recommend responses to organizational decision-makers.

The Citation

Olaniyan, R., Rakshit, S., & Vajjhala, N. R. (2023). Application of User and Entity Behavioral Analytics (UEBA) in the Detection of Cyber Threats and Vulnerabilities Management. In Prasenjit Chatterjee, Dragan Pamucar, Morteza Yazdani, Dilbagh Panchal (Eds.), Computational Intelligence for Engineering and Management Applications: Select Proceedings of CIEMA 2022 (pp. 419–426). Springer Nature Singapore. https://doi.org/10.1007/978-981-19-8493-8_32 Copy APA

What question does this paper answer?

How can artificial intelligence, and in particular user and entity behavioural analytics (UEBA), help organisations detect cyber threats and manage vulnerabilities as they become more dependent on open networks and cloud services?

What did the study find, in detail?

The paper positions UEBA as an AI-driven approach that scans large volumes of data across the Internet and an organisation’s systems to discover where attacks originate and to recommend responses to corporate decision-makers. It frames the growing threat landscape — viruses, data breaches and denial-of-service attacks — created by the Internet of Things, mobile technology and cloud platforms such as Amazon Web Services.

Why does it matter?

As organisations store sensitive and personal data on open networks and cloud infrastructure, the number of cyber risks grows with Internet use. UEBA offers decision-makers a data-driven way to detect anomalous behaviour by users and entities rather than relying only on signature-based defences.

Study at a glance

Abstract

Technological advancements such as the Internet of Things, mobile technology, and cloud computing are embraced by organizations, individuals, and society. The world is becoming more reliant on open networks, which fosters global communication and cloud technologies like Amazon Web Services to store sensitive data and personal information. This changes the danger landscape and opens new opportunities. As the number of people who use the Internet grows, so does the number of cyber risks and data security challenges that hackers pose. A cybersecurity threat is an action that aims to destroy or damage data, steal data, or otherwise disrupt digital life. Computer viruses, data breaches, and Denial of Service (DoS) assaults are all examples of cyber dangers. We’d be witnessing a notion of scanning enormous volumes of data across the internet if we described how AI systems might discover where hacks came from and recommend solutions to decision-makers within the corporation.

Abstract as published in Computational Intelligence for Engineering and Management Applications: Select Proceedings of CIEMA 2022.

Keywords: Artificial intelligence; Machine learning; UEBA; Cybersecurity threats; Vulnerability; Security

Limitations

• A short conference paper (8 pages) whose abstract reports no empirical evaluation, dataset or performance metrics.

When this research may be relevant

This paper may be relevant to literature searches on UEBA and behavioural analytics in cybersecurity, AI and machine learning for threat detection, vulnerability management in cloud-dependent organisations, and introductory or review-level treatments of AI in security operations.

Research topics addressed: user and entity behaviour analytics; UEBA; cyber threat detection; vulnerability management; artificial intelligence in cybersecurity; machine learning; cloud security; data breaches; denial of service; insider threats

How to cite

Olaniyan, R., Rakshit, S., & Vajjhala, N. R. (2023). Application of User and Entity Behavioral Analytics (UEBA) in the Detection of Cyber Threats and Vulnerabilities Management. In Prasenjit Chatterjee, Dragan Pamucar, Morteza Yazdani, Dilbagh Panchal (Eds.), Computational Intelligence for Engineering and Management Applications: Select Proceedings of CIEMA 2022 (pp. 419–426). Springer Nature Singapore. https://doi.org/10.1007/978-981-19-8493-8_32

@inproceedings{olaniyan2023ueba,
  title = {Application of User and Entity Behavioral Analytics (UEBA) in the Detection of Cyber Threats and Vulnerabilities Management},
  author = {Olaniyan, Rahma and Rakshit, Sandip and Vajjhala, Narasimha Rao},
  booktitle = {Computational Intelligence for Engineering and Management Applications: Select Proceedings of CIEMA 2022},
  series = {Lecture Notes in Electrical Engineering},
  editor = {Prasenjit Chatterjee and Dragan Pamucar and Morteza Yazdani and Dilbagh Panchal},
  pages = {419--426},
  year = {2023},
  publisher = {Springer Nature Singapore},
  doi = {10.1007/978-981-19-8493-8_32},
  url = {https://doi.org/10.1007/978-981-19-8493-8_32}
}