Capital One Services, LLC

Peter Terrana

Vannia Gonzalez Macias

Scott Garcia

Hao Liu

Rajesh Kanna Durairaj

Daniel W File

Phanindra Rao

Adam Beauregard

Dan File

Michal Hyrc

Aspects described herein allow for systems and methods to monitor production changes to resources in a cloud computing environment and determine whether those changes were performed in accordance with a change management policy. A change order monitoring application receives data from cloud computing audit logs to detect infrastructure changes and combines that data with application information to determine which application was affected. The change order monitoring application then uses a machine learning algorithm to cluster multiple change events together when it is likely that the change events were part of the same change. If cluster of change activity does not appear to be authorized, the change order monitoring application sends an alert to a change management team and an application team to get more information about the activity.

Change monitoring and detection for a cloud computing environment

Methods and systems are described herein for improving anomaly detection in timeseries datasets. Different machine learning models may be trained to process specific types of timeseries data efficiently and accurately. Thus, selecting a proper machine learning model for identifying anomalies in a specific set of timeseries data may greatly improve accuracy and efficiency of anomaly detection. Another way to improve anomaly detection is to process a multitude of timeseries datasets for a time period (e.g., 90 days) to detect anomalies from those timeseries datasets and then correlate those detected anomalies by generating an anomaly timeseries dataset and identifying anomalies within the anomaly timeseries dataset. Yet another way to improve anomaly detection is to divide a dataset into multiple datasets based on a type of anomaly detection requested.

Anomaly detection in a split timeseries dataset

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.

Automatic selection of data for target monitoring

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.

Automatic segmentation using hierarchical timeseries analysis

Anomaly detection data workflow for time series data

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Mechanicsville, VA, United States of America

Peter Terrana