Securing Cloud Infrastructure Against Privilege Escalation Attacks with  Machine Learning-Based Detection Systems

E. Vinod Kumar, Vishnu Satheesh, S.Sravani, Ch.Saikrishna, B. Chandana, M.Varshitha

Securing Cloud Infrastructure Against Privilege Escalation Attacks with Machine Learning-Based Detection Systems

Authors

E. Vinod Kumar, Vishnu Satheesh, S.Sravani, Ch.Saikrishna, B. Chandana, M.Varshitha

Keywords:

Cloud Security, Privilege Escalation Attacks, Machine Learning, Anomaly Detection, Ensemble Learning, Intrusion Detection System, Random Forest, XGBoost, Cloud Infrastructure Protection.

Abstract

Privilege escalation attacks represent a critical security threat in cloud computing environments,enabling unauthorized users to gain elevated access rights and compromise sensitive data and systemintegrity. Traditional detection mechanisms, primarily based on manual monitoring and rule-basedanomaly detection, often suffer from high false-positive rates, limited scalability, and delayed response, making them inadequate

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

E. Vinod Kumar, Vishnu Satheesh, S.Sravani, Ch.Saikrishna, B. Chandana, M.Varshitha. (2025). Securing Cloud Infrastructure Against Privilege Escalation Attacks with Machine Learning-Based Detection Systems . Journal of Computational Analysis and Applications (JoCAAA), 34(5), 429–445. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/4622