Enhancing Cloud Security with MachineLearning: Detection and Mitigation of Privilege Escalation Attacks

Cloud security is a critical concern as cyber threats, particularly privilege escalation attacks, continue to rise. This paper presents a machine learning-based approach for detecting and mitigating privilege escalation attacks in cloud environments. The proposed system utilizes classification algorithms, including LightGBM, AdaBoost, XGBoost, and Random Forest, to identify malicious activities. The study demonstrates how these models improve threat detection accuracy and response efficiency. Experimental results show that LightGBM outperforms other models in detecting privilege escalation attacks with high precision.