Study and Investigation of Cloud Based Security Policies Using Machine Learning Techniques

In recent days, most organizations have started to use cloud environments and various cloud services. To secure and secure the operations that an organization performs in the cloud, it is important to provide a secure and stable environment solution in cloud wind. Existing methods such as linear regression and support vector machines attempt to improve cybersecurity in the marketplace by proving the static behavior of the cloud to predict threats. There are security concerns with CC and edge computing, including client-side vulnerabilities and authentication organizations slowing the adoption of fast computing models. In this review article, researchers examine CC security threats, issues, and solutions implemented for one or more machine learning algorithms. The main purpose of this report is to develop next generation cloud security using machine learning algorithm like KNN which can provide an automatic and precise way to improve the cloud security environment. Machine learning not only focuses on finding and recognizing patterns in sensitive data, but can also provide solutions with holistic algorithms to protect business data in any cloud application. Researchers analyzed various machine learning algorithms to tackle air safety issues, including supervised, unsupervised, and semi-supervised, and then compared the performance advantages of each system against its features, strengths and weaknesses. However, in the future, techniques such as data mining and data clustering are used to improve classification, while more advanced techniques can be used to analyze traditional data. After reviewing a few algorithms, it's best to use encryption and cryptography techniques along with recommended algorithms to improve the performance and security of your data.