Methodology for Safeguarding Cloud Server from Web Application Attacks

This paper describes a thorough methodology for protecting cloud servers from online application threats, with the goal of improving overall speed and security. To reduce potential risks and vulnerabilities, the proposed strategy involves using multiple techniques and best practices. To detect and prevent assaults, the methodology emphasizes the use of safe programming techniques, routinely updated security patches, strong authentication mechanisms, and systems for intrusion detection. Furthermore, it compares the performance of three common machine learning methods, namely Logistic Regression, a Random Forest, and Decision Tree, in detecting and recalling probable assaults on the cloud server. The investigation focuses on assessing each algorithm's recall metrics to establish its ability to correctly recognize and classify dangerous activity. The outcomes of this study will be useful in determining the best algorithm for protecting cloud servers and assuring that they are resilient against web application threats.