AI-Enhanced Cloud Security Framework for IoT Networks Using a Predictive Analytics Approach

Abstract

The use of IoT with cloud computing has seen a massive improvement when it comes to data processing as well as connectivity. However, this has its own set of security problems like violation of access, leakage of information and issues with respect to scaling up. These challenges are fundamental and complex, and so this paper seeks to introduce an AI-cloud security framework that incorporates the use of predictive analytical functions. It is used to provide a real-time measure of security threats in IoT networks with a view of safeguarding both the data and the devices used. The values of precision and recall were 95 percent and 90 percent, respectively, with an overall anomaly detection rate higher than the previous threat detection methods. The framework also unveiled overall improvements in response time and false positive rates over the conventional methods. Such findings suggest that implementing $A I$ and predictive analytics into cloud security frameworks for IoT networks are highly conceivable. Concerning future work, the research underlines the need for security solutions that can adapt to IoT’s increasing connectivity requirements in numerous applications and would open the path for the next developments in IoT protection frameworks.