Enhancing Cloud Security with Machine Learning-Based Detection of Privilege Escalation Attacks

Privilege escalation attacks are becoming increasingly dangerous, especially since more companies are rapidly moving to cloudcomputing. Vulnerabilities in the system become ways for attackers to sneak into high-security areas, causing information loss,system failure and danger to your cloud. In this project, we are working on improvements that allow detecting and tackling privilege escalation attacks that occur in cloud environments. Random Forest, K-Nearest Neighbours Classifier, Support Vector Machine (SVM), Decision Tree Classifier and Gradient Boosting Classifier are some of the several algorithms we use to detect and classify different types of attacks. SVM is used as the primary method because it performs well compared to other algorithms. For the backend, Python is used, Django for integration, MySQL for storing information in databases and HTML/CSS for how the webpages look. Pandas, NumPy and Scikit-learn which are used for data processing, also allow you to put models in place for real-time detection. Thanks to machine learning, the system fortifies cloud security and can handle different amounts of incoming attacks. They suggest that the system is able to detect hazards accurately and handle new security challenges, ensuring the security and trustworthiness of the cloud now and in the future.