Hybrid Statistical Modeling for Anomaly Detection in Multi-Key Stores Based on Access Patterns

Published: 01 Jan 2024, Last Modified: 04 Feb 2025IoTBDS 2024EveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: Anomaly detection in datasets with massive amounts of sparse data is not a trivial task, given that working with high intake data in real-time requires careful design of the algorithms and data structures. We present a hybrid statistical modeling strategy which combines an effective data structure with a neural network for Gaussian Process Modeling. The network is trained in a residual learning fashion, which enables learning with less parameters and in fewer steps.
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