A network data analytics method and apparatus are provided. The method includes receiving, by a network repository function network element, a first message from a first device, where the first message is used to discover a network data analytics function network element, and the first message carries a first analytics identifier and first analytics filtering information.

One example method includes connecting to a server component, transmitting, to the server component, information concerning a hardware configuration associated with an asset having a capability that is fully utilized during a first time period and the capability is idle during a second time period, receiving, from the server component, cluster connection information, and using the cluster connection information to temporarily connect the asset to the cluster as a node of the cluster so that the capability is available during idle time to perform a workload of the cluster.

An example system for Predictive Services Management (PSM) in cable network environments is provided and includes a data collector located in a cable network that captures multi-tone signals traversing the cable network, a data repository located in a cloud network, and a central server having PSM algorithms configured for: retrieving key performance indicators from the multi-tone signals; identifying a fault signature based on the key performance indicators, the fault signature being identified based on phase domain analysis of a channel response; accessing the data repository for geographical information; determining a location of a fault in the cable network based on the fault signature and the geographical information; accessing the data repository for device information; determining a type of fault based on the location of the fault and the device information; and activating repair and maintenance activities based on the type of fault, location of the fault and the fault signature.

One embodiment is a system including a data collector located in a cable network for capturing multi-tone signals traversing the cable network; a data repository located in a cloud network and having an interface for communicating with the data collector and for storing the multi-tone signals captured by the data collector and network data associated with the cable network; and a central server including a memory element storing Predictive Services Management (PSM) algorithms comprising instructions and associated data and a processor operable to execute the PSM algorithms. The central server is configured for detecting a fault in the cable network and identifying a segment associated with the fault; determining a maximum tap magnitude for the fault; calculating an aggregate tap magnitude for the fault; and classifying a severity of the fault based at least in part on the maximum tap magnitude and the aggregate tap magnitude.

A data processing method is performed by an edge device acquiring collected data from a collection target and a first computer capable of communicating with the edge device. The method includes: a first calculation process of, by the edge device, storing the collected data in a secure region to which referring of internally stored information from outside is not allowable and calculating first data which has a data amount less than the collected data and is irreversible in the secure region based on the stored collected data; a first communication process of, by the edge device, transmitting the first data calculated through the first calculation process to the first computer; and a second calculation process of, by the first computer, calculating second data based on the first data transmitted from the edge device through the first communication process.

In one embodiment, a networking device at an edge of a network generates a first set of feature vectors using information regarding one or more characteristics of host devices in the network. The networking device forms the host devices into device clusters dynamically based on the first set of feature vectors. The networking device generates a second set of feature vectors using information regarding traffic associated with the device clusters. The networking device models interactions between the device clusters using a plurality of anomaly detection models that are based on the second set of feature vectors.

A system and method for network cybersecurity analysis that uses user and entity behavioral analysis combined with network topology information to provide improved cybersecurity. The system and method involve gathering network entity information, establishing baseline behaviors for each entity, and monitoring each entity for behavioral anomalies that might indicate cybersecurity concerns. Further, the system and method involve incorporating network topology information into the analysis by generating a model of the network, annotating the model with risk and criticality information for each entity in the model and with a vulnerability level between entities, and using the model to evaluate cybersecurity risks to the network. Risks and vulnerabilities associated with user entities may be represented, in part or in whole, by the behavioral analyses and monitoring of those user entities.

A system and method for a flexible, high-speed Managed Detection and Response platform that ingests, parses, normalizes, monitors, and correlates nearly any log source or security tool output. The MDR comprising of a declarative connector service that tags events with appropriate data source labels to facilitating data isolation, proper handling, and provenance across multiple customers and security products but otherwise aggregate alerts into a single data stream for consumption by the MDR SOC operators. A connector service further provides a programmatic (API-based) means to interchange data securely across environments. Event data is aggregated by the Managed Detection and Response platform that then utilizes enhanced log ingest capabilities to process the data allowing SOC operators to be able to write rules against the alerts.

Techniques for improving data transmission in teleoperation systems including a method for dynamic packet routing. The method includes identifying an optimal channel of a plurality of channels based on a network connectivity status of a system and historical connectivity data related to a current location of the system, wherein the system includes a plurality of network authorization devices, wherein each network authorization device is configured to enable communications via an associated channel; and routing packets to the optimal channel using a network authorization device of the plurality of network authorization devices that is associated with the optimal channel.

In one embodiment, a device in a network monitors performance data for a first predictive model. The first predictive model is used to make proactive decisions in the network. The device maintains a supervisory model based on the monitored performance data for the first predictive model. The device identifies a time period during which the supervisory model predicts that the first predictive model will perform poorly. The device causes a switchover from the first predictive model to a second predictive model at a point in time associated with the time period, in response to identifying the time period.