Adaptive Insider Threat Detection in Cloud Platforms Using Ensemble  Machine Learning Models

Syeda Arshiya Nausheen, P. Pavani, S. Sindhuja, Ch. Adarsh, G. Bhavan Kumar

Adaptive Insider Threat Detection in Cloud Platforms Using Ensemble Machine Learning Models

Authors

Syeda Arshiya Nausheen, P. Pavani, S. Sindhuja, Ch. Adarsh, G. Bhavan Kumar

Keywords:

Cloud Security, Privilege Escalation, Ensemble Learning, Anomaly Detection, Cybersecurity

Abstract

Insider attacks in cloud environments pose major security risks by compromising the confidentialityand integrity of critical data through unauthorized access escalation. Conventional defense mechanisms,such as manual monitoring and rule-based anomaly detection, are slow, inefficient, and incapable of identifying complex patterns within large-scale cloud activity logs

References

S, A., D, S., & G, P. (2024). Malicious insider threat detection using variation of sampling methods for anomaly detection in cloud environment. In Computers and Electrical Engineering (Vol. 105, p. 108519). Elsevier BV.

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Published

2025-05-25

How to Cite

Syeda Arshiya Nausheen, P. Pavani, S. Sindhuja, Ch. Adarsh, G. Bhavan Kumar. (2025). Adaptive Insider Threat Detection in Cloud Platforms Using Ensemble Machine Learning Models . Journal of Computational Analysis and Applications (JoCAAA), 34(5), 357–366. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/4615