A Multivariate Statistical Approach To Network Anomaly Detection In Cloud Data Migration

Abstract— A. Context Automation of migration processes in cloud environments is complex and challenging due to multiple parameters, includ ing environment monitoring and forecasting. Ensuring smooth migration requires accurate anomaly detection and proactive measures. B. Objective This paper aims to address migration-related issues by detect ing network anomalies in cloud environments using quantitative approaches. It focuses on improving operational efficiency and security through advanced anomaly detection techniques. C. Methods We present a comprehensive analysis of metric anomaly detec tion techniques tailored for cloud computing environments. The study explores machine learning models and statistical analysis methods to enhance precision and speed in anomaly detection. D. Results Our analysis highlights the unique challenges posed by dy namic, scalable, and distributed cloud systems. We demonstrate how metric anomalies impact performance and security, and evaluate emerging methodologies for effective detection. E. Conclusion By addressing these critical concerns, the paper provides practical insights and strategies for cloud service providers, IT professionals, and researchers. It serves as a guide for best practices in metric monitoring and building robust, adaptive, and resilient cloud infrastructures.