Abstract: Systems, methods, and devices that relate to configuration validity assessment of systems and devices are disclosed. In one example aspect, a system is caused to receive a first script corresponding to a first device of a first type, determine a first hierarchical structure associated with the first script, retrieve a statistical model trained using configurations of devices of the first type, determine, based on the statistical model, probabilities indicating a likelihood that each node of the first hierarchical structure associated with the first script corresponds to a valid configuration, perform a comparison between the probabilities and a confidence threshold, and, based on the comparison, selectively approve or reject the first script corresponding to the first device.

Abstract: A computer system obtains documents containing telecommunications network equipment configurations. Using a chat bot interface, the system captures an output type instruction specifying desired artifact features (e.g., format, timestamp, keywords, location, user role) for constructing or maintaining the equipment. The system uses a first AI model to identify reference documents from the obtained documents by comparing vector representations of the documents with the instruction. The reference documents share one or more features with the artifact. A second model uses the reference documents to generate a response to the output type instruction. The chat bot interface displays both the generated artifact and the reference documents.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.