Systems and methods for proactively alerting administrators of upcoming or possible network outages include a server which receives metrics for usage of one or more networks for each workspace application of plurality of workspace applications of a plurality of endpoints across a plurality of different locations of an enterprise. The server may determine a network download speed for each location of the plurality of different locations according to the metrics for each workspace application. The server may generate an alert to be provided to a device of a user associated with a first location of the plurality of different locations responsive to the network download speed for a network of the one or more networks falling below a threshold.

Various aspects involve a lagged prediction model trained for risk assessment or other purposes. For instance, a risk assessment computing system receives a risk assessment query for a target entity and provides an input predictor record for the target entity to a lagged prediction model. The input predictor record includes a first group of lagged values from a first time-series attribute associated with the target entity. The lagged prediction model is trained by implementing a group feature selection technique configured to select the first time-series attribute as input and to deselect a second time-series attribute associated with the target entity. The risk assessment computing system computes an output risk indicator from the input predictor record and transmits the output risk indicator to a remote computing system. The output risk indicator can be used to control access by the target entity to one or more interactive computing environments.

Various embodiments comprise systems, methods, architectures, mechanisms and apparatus for controlling Quality of Service (QoS) within a service provider network by retrieving from the network QoS related data, processing the retrieved QoS related data via one or more time series prediction algorithm to determine QoS prediction data, and responsively generating network management or configuration commands adapted to ensure continued services delivery in accordance with QoS requirements.

Novel tools and techniques are provided for utilizing blockchain to implement named data networking. In various embodiments, a computing system might determine whether a cache that is communicatively coupled to the computing system contains data that is responsive to a first request received from a user. If so, the computing system might retrieve and send (to the client device) data that is responsive to the received first request. If not, the computing system might send, to a blockchain system, a second request for identifying a blockchain containing a block containing data responsive to the received first request. In response to identifying such a blockchain, the computing system might receive a copy of the identified blockchain; might abstract, from the identified blockchain, the block containing the data responsive to the received first request; might abstract the data from the identified block; and might send the data to the client device.

The packet capture manager uses a multi-tiered storage for storing captured network traffic. Captured packets are stored on a primary storage with a time-to-live according to a retention policy. The packet capture manager receives instructions from one or more network monitoring devices identifying one or more captured packets as packets of interest. The packet capture manager flags the identified packets as packets of interest, moves the flagged packets to a secondary storage, and changes the TTL of the moved packets. A machine learning model analyzes historical data of the instructions received from the one or more network monitoring devices. The packet capture manager uses the machine learning model to identify packets of interest and move identified packets to the secondary storage without specific instructions from a network monitoring device.

Disclosed are various approaches for automating the detection and identification of anomalous devices in a management service. Device check-ins are received by a management service and housed in a data store. The quantity of device check-ins over various time periods can be analyzed using various approaches to identify anomalous devices.

Disclosed are various approaches for automating the detection and identification of anomalous devices in a management service. Device check-ins are received by a management service and housed in a data store. The quantity of device check-ins over various time periods can be analyzed using various approaches to identify anomalous devices.

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.

To be able to adequately provide desired services over a 5G mobile service network, the 5G communication infrastructures requires a much-improved flexibility in resource management. Network operators are foreseen to deploy network slicing, by isolating dedicated resources and providing customised logical instances of the physical infrastructure to each service. A critical operation in performing management and orchestration of network resources is the anticipatory provisioning of isolated capacity to each network slice. Accordingly, it is necessary to obtain an estimate of service level demands. However, the estimation of such service level demands is typically obtained via deep packet inspection, which is a resource intensive and time-consuming process. Therefore, it is typically not possible to provide updated accurate estimates at a frequency suitable for use in accurate prediction of a future per-service traffic consumption, without an undesirable level of computational and time resources being required. The present invention provides a distributed network traffic data decomposition method which makes use of a neural network to provide an accurate future per-service traffic consumption prediction without deep-packet inspection or another resource intensive analysis method.

An anomaly detection method and device, a terminal and a storage medium are disclosed. The method may include: generating at least one clustering set of objects based on configuration data and performance indicator data of the objects; determining an algorithm configuration parameter corresponding to each clustering set based on a preset anomaly detection algorithm and the performance indicator data corresponding to the objects in the clustering set; and determining, based on the algorithm configuration parameter, abnormal performance indicator data of the objects in the corresponding clustering set, so as to determine abnormal objects based on the abnormal performance indicator data.