T-Mobile USA, Inc.

Jan Olav Opedal

Srikrishna Srinivasan

Yanbing Su

Pradeep Cruz

Yinjun Zhu

Suchit Satpathy

A fraud detection system to proactively identify fraudulent transactions. The system receives an analyst-generated report for a transaction and trains a model using features from the feature types identified in analyst-generated reports. The system generates a fraud likelihood score for each requested transaction, compares it to the analyst-generate assessment, and retrains the model based on the difference.

Dynamic machine learning models for detecting fraud

Embodiments include a method for vulnerability management of a computer system. The method includes collecting vulnerability information over a network from a publishing source. The vulnerability information includes a known vulnerability of a first computer asset, where at least some of the vulnerability information is a set of cybersecurity vulnerabilities and exposures (CVEs) published online. Further, at least some of the CVEs is in a human-readable format. The method further includes collecting system information of the computer system subject to the vulnerability management, where the system information includes information about a second computer asset of the computer system. The method further includes processing the collected vulnerability information and the collected system information by interpreting the human-readable CVEs and correlating the interpreted CVEs with the collected system information. A potential vulnerability of the second computer asset is identified based on the correlation between the interpreted CVEs and the collected system information.

Adaptive vulnerability management based on diverse vulnerability information

A network assessment system for generating subjective quality ratings for Internet protocol multimedia subsystem (IMS) data sessions in real-time. The system measures real-time telecommunications network data during an IMS data session, processes the data using a trained model, and receives a subjective quality rating usable to modify the performance of a node in instances where the node does not typically have an associated subjective quality rating or in instances where the node is not associated with a standardized subjective quality rating.

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Ellensburg, WA, United States of America

Jan Olav Opedal