Various organizations implement cloud computing to store a significant amount of data in the clouds. Therefore, it may be necessary to secure data that may also be in the form of text, audio, video, and many other types. There are a number of algorithms created with the help of employing the researchers' methods for protecting the information in the cloud. In this paper implementation of Adaptive cloud security framework for cloud computing monitoring system applications. Cloud security has emerged as one of the most crucial challenges in cloud computing as a result of the industry's continued expansion. For instance, it is challenging to ensure the security of data hosted on a cloud platform since such data may be attacked. As a result, we must give consideration to the problem of how to safeguard data stored on the cloud. Data monitoring is a vital step in order to secure data. We create an autonomic computing-based cloud data monitoring system on the cloud platform, checking to see whether the data is out of the ordinary throughout the cycle and reviewing the security of the data in light of the results. In this paper, the feasibility of the scheme can be verified through simulation. The findings demonstrate that the suggested technique can properly assess the level of anomalous data and can adjust to the dynamic change in cloud platform load. Meanwhile, it increases the precision and timeliness of monitoring by automatically altering monitoring frequency. Additionally, it can lower the system's monitoring costs during routine operation.

Cloud Computing, Cloud Security, Cloud Threats, Cloud Vulnerabilities, Data Security.