Privilege Escalation Attack Detection Mitigation in Cloud Using Machine Learning
Nikhil Nikhil, Sai Abhiram, Revanth Reddy, Mr. Praveen S R · International Journal of Research Publication and Reviews · 2025
The proliferation of smart gadgets has led to an exponential increase in cyberattacks, which has made cybersecurity more difficult.Because of its centralized architecture and the volume of data that is shared between businesses and cloud providers, cloud computing poses hazards even as it is revolutionizing corporate processes.With legal access, malicious insiders are a serious risk since they can cause a great deal of harm by abusing their powers.In order to detect privilege escalation assaults, this paper suggests a machine learning-based insider threat detection system.To increase prediction accuracy, the system integrates several models through ensemble learning.A CERT insider threat dataset was used to test four machine learning algorithms: Random Forest (RF), AdaBoost, XGBoost, and LightGBM.With a 97% accuracy rate, LightGBM outperformed RF, AdaBoost, and XGBoost, which had respective accuracy rates of 86%, 88%, and 88.27%.LightGBM was the best overall, but in some assault scenarios, RF and AdaBoost fared better, indicating that combining algorithms is necessary for stronger, more reliable classification.