ENHANCING CLOUD SECURITY: EMPOWERING MACHINE LEARNING TO COMBAT PRIVILEGE ESCALATION ATTACKS

Abstract

This project is based on machine learning to ensure cloud security is made more resilient by targeting and preventing privilege escalation attacks. The probability of the attack of privilege escalation increases with the increased population of cloud users. The project seals gaps in the access control of employees to the services offered by the clouds in order to make the entire system more secure. In the project, machine learning is used to detect and prevent privilege escalation attacks in real time. Some of the tools include LightGBM, Random Forest, Adaboost and Xgboost which can assist in maintaining your defences against emerging threats. The users and organisations are assured of the safety of their data thus creating confidence upon cloud computing. The security improvements by the project will make cloud service providers and businesses feel more secure as they are online. The system can better detect and prevent attempts of privilege escalation with the help of a Voting Classifier, which integrates predictions by the Decision Tree, Random Forest and Support Vector Machine using a soft voting strategy. Moreover, user testing can be simplified with the help of a user-friendly Flask application with SQLite integration that also includes secure signup and signin functions that can be tested and used in the real life.