Strengthening Cloud Security For Unauthorized Access Prevention

Due to the increased frequency of recent attacks, there are serious cyber security issues as a result of smart device usage. Cloud computing has brought about major changes in the commercial world. Centralization, however, increases the danger of important data breaches and makes it impossible to deploy dispersed services like security systems due to the massive volume of data that is transmitted between businesses and cloud service providers. Insider attacks are a serious risk to the business since they can cause more damage and have greater access. Finding different strange events that could point to anomalies and security issues with privilege escalation is the aim of this technique. Through the use of ensemble learning to combine multiple models, the strategy can improve machine learning outcomes and prediction performance. The proposed standard uses a modified dataset taken from the CERT dataset to assess the Support Vector Machine (SVM) machine learning technique for insider attack classification. Utilizing patterns of behavior and activity, such as resource usage, file access times, and login times, the system builds a profile of typical behavior and looks for deviations. This strategy can assist companies in identifying insider threats early on and avert serious harm to their priceless resources and data.