Machine learning for secure API obfuscating in cloud applications with app cloaking techniques

App cloaking is a powerful technique for safeguarding cloud-based applications against cyber threats by obfuscating sensitive data. It enables organizations to secure their applications from public internet by concealing critical data and hiding application communication routes. With app cloaking, organization can isolate their applications and hide their IP addresses from the internet, making them invisible to unauthorized users. This paper leverages a machine learning (ML) approach to predict the effectiveness of cloaking techniques in cloud-based applications, enabling organizations to enhance their security measures. The proposed ML techniques analyze various features in a sample AWS dataset, including API Endpoint, AWS Service Type, Traffic Volume, Obfuscation Method, Detection Attempts, Obfuscation Complexity, Threat Detected, Response Time Impact, and Observed Attack Outcome, to evaluate the effectiveness of Cloaking techniques under different circumstances.