Machine learning for secure API obfuscating in cloud applications with app cloaking techniques
Information Technology Management, Central Washington University, Ellensburg, WA, USA.
Research Article
Open Access Research Journal of Science and Technology, 2025, 15(01), 012–019.
Article DOI: 10.53022/oarjst.2025.15.1.0111
Publication history:
Received on 29 July 2025; revised on 09 September 2025; accepted on 11 September 2025
Abstract:
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.
Keywords:
Machine Learning; User Behavior Analytics; Cybersecurity; Anomaly Detection; Threat Identifi
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Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0