A Comprehensive Framework for Real-Time Anomaly Detection Using Data Science Techniques in Multi-Cloud Systems

Robert Taylor

Authors

Robert Taylor Software Engineer, UK Author

Keywords:

anomaly detection, multi-cloud systems, real-time analytics, data science, machine learning, cloud security, distributed systems

Abstract

The proliferation of multi-cloud systems presents significant challenges in ensuring operational efficiency and security. Anomalies within these systems can signal potential performance bottlenecks, security breaches, or configuration errors. This paper proposes a comprehensive framework for real-time anomaly detection leveraging state-of-the-art data science techniques. By combining machine learning, statistical methods, and cloud-native tools, the framework addresses the complexities of distributed and heterogeneous cloud environments. The study integrates insights from existing literature and empirical analysis to demonstrate the framework's efficacy. Experimental evaluations on multi-cloud datasets validate its ability to identify anomalies with high accuracy and low latency.

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