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

Summary

Olaniyan et al. (2023) examined, in a short conceptual and applied overview, how artificial intelligence, and in particular user and entity behavioural analytics (UEBA), can help organisations detect cyber threats and manage vulnerabilities as they become more dependent on open networks and cloud services. They position UEBA as an AI approach that scans large volumes of system and network data to discover where attacks originate and to recommend responses to decision-makers within the organisation. The abstract reports no empirical evaluation, dataset or performance metrics.

Practical implication. Decision-makers can use user and entity behavioural analytics (UEBA) as a data-driven way to detect anomalous behaviour by users and entities, rather than relying only on signature-based defences.

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 P. Chatterjee, D. Pamucar, M. Yazdani, & D. 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

The summary, key findings, methods, implications and relevance notes on this page 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 study in detail

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.

Methods and data

Definitions and key terms

Cybersecurity threat

An action that aims to destroy or damage data, steal data, or otherwise disrupt digital life, with computer viruses, data breaches and Denial of Service (DoS) attacks given as examples. — as used in Olaniyan et al. (2023)

User and entity behavioural analytics (UEBA)

An AI approach, as the paper positions it, that scans large volumes of system and network data to discover where attacks originate and to recommend responses to decision-makers within the organisation. — as used in Olaniyan et al. (2023)

All key terms across the publications →

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.

Practical implications

• Decision-makers in organisations that store sensitive and personal data on open networks and cloud infrastructure can use UEBA to detect anomalous behaviour by users and entities instead of relying only on signature-based defences.
• Organisations can, according to the paper, use AI systems with user and entity behavioural analytics to scan enormous volumes of data, discover where attacks originated and recommend solutions to their decision-makers.
• According to the paper, reliance on open networks and cloud services such as Amazon Web Services to store sensitive data changes the threats that organisations face.

Drawn from Olaniyan et al. (2023), Computational Intelligence for Engineering and Management Applications: Select Proceedings of CIEMA 2022, Lecture Notes in Electrical Engineering, vol. 984, pp. 419–426, Springer Nature Singapore.

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

APA 7

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 P. Chatterjee, D. Pamucar, M. Yazdani, & D. 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_32Copy APA citation

MLA 9

Olaniyan, Rahma, et al. “Application of User and Entity Behavioral Analytics (UEBA) in the Detection of Cyber Threats and Vulnerabilities Management.” Computational Intelligence for Engineering and Management Applications: Select Proceedings of CIEMA 2022, edited by Prasenjit Chatterjee et al., Springer Nature Singapore, 2023, pp. 419–26. https://doi.org/10.1007/978-981-19-8493-8_32.Copy MLA citation

IEEE

R. Olaniyan, S. Rakshit, and N. R. Vajjhala, “Application of User and Entity Behavioral Analytics (UEBA) in the Detection of Cyber Threats and Vulnerabilities Management,” in Computational Intelligence for Engineering and Management Applications: Select Proceedings of CIEMA 2022, P. Chatterjee, D. Pamucar, M. Yazdani, and D. Panchal, Eds. Springer Nature Singapore, 2023, pp. 419–426, doi: 10.1007/978-981-19-8493-8_32.Copy IEEE citation

@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 = {Chatterjee, Prasenjit and Pamucar, Dragan and Yazdani, Morteza and Panchal, Dilbagh},
  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}
}

TY  - CPAPER
TI  - Application of User and Entity Behavioral Analytics (UEBA) in the Detection of Cyber Threats and Vulnerabilities Management
AU  - Olaniyan, Rahma
AU  - Rakshit, Sandip
AU  - Vajjhala, Narasimha Rao
ED  - Chatterjee, Prasenjit
ED  - Pamucar, Dragan
ED  - Yazdani, Morteza
ED  - Panchal, Dilbagh
T2  - Computational Intelligence for Engineering and Management Applications: Select Proceedings of CIEMA 2022
T3  - Lecture Notes in Electrical Engineering
PY  - 2023
DA  - 2023/04/30
VL  - 984
SP  - 419
EP  - 426
PB  - Springer Nature Singapore
SN  - 1876-1100
DO  - 10.1007/978-981-19-8493-8_32
UR  - https://www.narasimharao.net/research/ueba-user-entity-behavioral-analytics-cyber-threat-detection/
LA  - en
ER  - 

Links and files

• DOI: https://doi.org/10.1007/978-981-19-8493-8_32
• Publisher’s page: https://link.springer.com/chapter/10.1007/978-981-19-8493-8_32
• This page as text: https://www.narasimharao.net/research/ueba-user-entity-behavioral-analytics-cyber-threat-detection/paper.md
• Permanent address of this page: https://www.narasimharao.net/research/ueba-user-entity-behavioral-analytics-cyber-threat-detection/