T-mobile Usa, Inc.

T-mobile Innovations LLC

Yanbing Su

Srikrishna Srinivasan

Jan Olav Opedal

Pradeep Cruz

Yinjun Zhu

Suchit Satpathy

Faisal Waris

Pradeep Christopher Cruz

Jia Yan

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.

Machine learning model for assessing network performance

In various examples, description data may be used by an engine to correlate a subset of alarms representing a network incident. A machine learning model may then be used to predict a likelihood that one or more of the alarms within the subset is a root cause of the network incident. This root cause may then be displayed on a graphical user interface. As a result, alarm fatigue experienced by network administrators may be reduced.

Root cause incident detection using an alarm correlation engine

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Frisco, TX, United States of America

Yanbing Su