Identifying Cyberattacks in Cloud ‎Computing Service Frameworks Through ‎Correlation-Driven Feature - GV, smitha; Khaiyum , Dr. Samitha; S, Yashaswini D.; N, Yadhunandan G.; Reddy, Yarramreddy Sai Kumar; S, Yogeshwar (International Journal of Basic and Applied Sciences, 2025)
Cloud computing infrastructures are often targeted by hacktivists due to their relatively ‎insecure service and delivery paradigms. This article addresses common attack vectors, such ‎as ransomware, insider threats, API breaches, and data leaks, that, if ignored, might ‎jeopardize sensitive data and interfere with important work. We provide a new method based ‎on correlation-based feature selection for identifying fraudulent activities in cloud systems. ‎Using Pearson correlation analysis, we drastically cut down on redundant features on the ‎security dataset, yielding promising results for threat identification. Our feature selection ‎method’s effectiveness is empirically supported, and we demonstrate that the classification ‎algorithms perform better when using the improved tool of the optimized data set than when ‎using the original feature set. In this research, a unique approach to intelligent data pre-‎processing for cloud security enhancement is presented‎.