Model-Based Cloud Surveillance for Enhanced Data Protection

Abstract

This proposes a novel approach to securing cloud environments by generating automated monitors from behavioral models. Using machine learning algorithms, the system detects anomalies and potential threats in real-time, offering an advanced security layer without relying solely on traditional encryption. The approach also integrates watermarking techniques to validate data integrity during transmission and storage. Unlike existing solutions that struggle with key management and performance issues, this framework provides seamless, scalable monitoring across cloud infrastructures. The implementation leverages tools such as OpenStack and Django, with support for RESTful API interactions. The results validate that our model-driven system is effective in detecting data breaches and protecting sensitive assets, making it a robust solution for modern cloud security challenges.