Probabilistic Anomaly Detection for Cloud Backend Environments
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Abstract
This study proposes an uncertainty-aware anomaly detection algorithm to address the challenges of dynamic coupling among multidimensional metrics, complex system dependencies, and diverse anomaly patterns in cloud backend environments. The method achieves robust modeling and accurate detection for high-dimensional non-stationary data by integrating temporal feature extraction, structural dependency modeling, and uncertainty quantification within a unified framework. A multi-scale temporal feature encoder is designed to capture both short-term fluctuations and long-term trends in system operations, while a dynamic graph mechanism models the evolving topological relationships among service nodes to enable structure-aware dependency learning. Furthermore, the model employs variational inference to perform probabilistic modeling in the latent space, estimating prediction confidence and uncertainty distributions to dynamically adjust detection thresholds and decision boundaries in complex environments. Experimental results show that the proposed algorithm maintains high detection accuracy and stability under highly dynamic conditions such as load surges, resource fluctuations, and network variations. It effectively reduces false positives and false negatives and demonstrates strong interpretability in anomaly propagation path modeling and risk identification. This research provides a scalable, interpretable, and adaptive detection framework for intelligent cloud backend operations, establishing a solid algorithmic foundation for system state awareness and risk management in complex environments.
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