CPC H04L 63/1425 (2013.01) [G06F 18/2411 (2023.01); G06N 20/10 (2019.01)] | 20 Claims |
1. A method comprising:
training an anomaly detection model to learn a plurality of anomaly regions within a multidimensional space including two or more dimensions of a computing application, wherein training the anomaly detection model comprises partitioning the multidimensional space into quadrants based on a quantile point within a set of training data;
storing a mapping between each respective anomaly region of the plurality of anomaly regions and a respective anomaly classifier from a set of anomaly classifiers;
evaluating a set of metrics for the computing application using the trained anomaly detection model to detect an anomaly in the computing application;
assigning, based at least in part on the mapping, a particular anomaly classifier to the anomaly from the set of anomaly classifiers;
performing a responsive action to address the anomaly based at least in part on the particular anomaly classifier.
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