Abstract: Methods and systems are described herein for determining auxiliary parameters within datasets, the auxiliary parameters being used to segregate the datasets such that anomaly detection may be performed on the segregated datasets. Based on anomaly detection, alert conditions may then be identified. In particular, a system may, using a machine learning model, determine for a particular target feature (e.g., a parameter being monitored) one or more auxiliary features (other parameters) that effect the values of that parameter and transmit the target feature and the auxiliary features in a message to a monitoring system indicating which features to monitor. The collected data may then be received by the system and transformed into a timeseries dataset, which may then be used to detect anomalies within the data and thereby identify any anomalous points.

Abstract: Methods and systems are disclosed for using a feature hierarchy with multiple levels with a different number of features at different levels to segment a dataset. The mechanism may use the first level of the hierarchy to segment a dataset into multiple datasets and then generate a timeseries dataset for each segment. Those timeseries datasets may be input into an anomaly detection model to identify a number of anomalies detected within those segments. Based on the number of anomalies not reaching a threshold, a second level of the hierarchy may be used to segment the dataset. Those segments may be input into the anomaly detection model to determine a number of anomalies for the second level. This process may continue until a level of the hierarchy is determined such that the number of anomalies reaches the threshold. The mechanism may then generate a security rule to deal with the anomalies.