Optum, Inc.

Ziqian Huang

William W Casey

Michael J Levin

Anne Jackson

Matthew H Murphy

Nicolaus Christiaan Thirion

Jack R Freier

Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for identifying stale or vibrant open-source packages by training a predictive machine learning model with a labeled dataset, wherein the labeled dataset is created by generating package-basis features and version-basis features based on repository data, generating package-basis clusters based on the package-basis features, generating version-basis clusters based on the version-basis features, and generating labels for the labeled dataset based on the package-basis clusters and the version-basis clusters.

Systems and methods for detecting usage of undermaintained open-source packages and versions

A method includes predicting, before a first period of time, a plurality of predicted event metric values and value ranges corresponding to a first plurality of events to occur over the first period of time based on a plurality of prediction models, determining an actual event metric value of the first plurality of events, and determining a plurality of model range multi-partition error (MRME) values indicative of an accuracy, precision, and relative range of the forecasting models based on the actual event metric values, value ranges, and the predicted event metric values. The method includes selecting one of the forecasting models based on the MRME values, determining a cyber security control limit based on a predicted event metric value or predicted event metric range by the selected forecasting model, and outputting an alert based on an actual event metric value being outside of the cyber security control limit.

Prediction model selection for cyber security

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Buffalo Grove, IL, United States of America

Ziqian Huang