Abstract: Detecting anomalous behavior using a browser extension, including: gathering first information describing activity associated with a user and generated by a browser extension on a user device; gathering second information describing activity associated with the user and generated by an application executed on the user device; and determining, based on the first information and the second information, whether the user has deviated from normal activity.

Abstract: Generating user-specific polygraphs for network activity, including: gathering information describing network activity associated with a user and generating, based on the information, a user-specific polygraph that includes one or more destinations associated with the network activity.

Abstract: Detecting deviations from typical user behavior, including: identifying a geographic location of a device that is associated with a user; determining device activity associated with the user; and detecting, based on a profile associated with the user, that the device activity associated with the user deviates from normal activity for the user.

Abstract: Detecting anomalous behavior of a device, including: generating, using information describing historical activity associated with a user device, a trained model for detecting normal activity for the user device; gathering information describing current activity associated with the user device; and determining, by using the information describing current activity associated with the user device as input to the trained model, whether the user device has deviated from normal activity.

Abstract: Detecting anomalous behavior using a browser extension, including: gathering first information describing activity associated with a user and generated by a browser extension on a user device; gathering second information describing activity associated with the user and generated by an application executed on the user device; and determining, based on the first information and the second information, whether the user has deviated from normal activity.

Abstract: Methods, systems, and products for offline workflows in an edge-based data platform, including: accessing log data describing activity associated with a user; generating, based on the log data, one or more alerts; and initiating, based on the one or more alerts, a workflow to acknowledge the one or more alerts by the user.

Abstract: This disclosure provides solutions for automatically grouping network devices (e.g., endpoints) into clusters based on device characteristics. In some aspects, the disclosed technology also provides solutions for generating user selectable queries based on cluster characteristics. A process of the disclosed technology can include steps for identifying one or more device characteristics associated with a first network device, identifying one or more cluster characteristics for each of a first cluster and a second cluster, and comparing the device characteristics associated with the first network device with the one or more cluster characteristics for the first cluster and the second cluster. The process can further include steps for adding the first network device to the first cluster based on the cluster characteristics for the first cluster and the device characteristics for the first network device. Systems and machine-readable media are also provided.

Abstract: Distinguishing user-initiated activity from application-initiated activity, including: gathering first information generated by a browser extension of a browser executed on a user device, wherein the first information describes activity associated with the browser; gathering second information generated by a client application executed on the user device, wherein the second information describes activity associated with the user device; and determining whether a user has deviated from normal activity by determining, based on the first information, whether at least a portion of the activity described in the second information comprises user-initiated activity or browser-initiated activity.

Abstract: The present disclosure relates to methods, systems, and non-transitory computer readable media for discovering policy scopes within an enterprise network and managing network policies for discovered policy scopes. In one aspect, a method includes identifying one or more communities of devices in an enterprise network; defining, from the one or more communities of devices, policy scopes in the enterprise network; generating a hierarchical representation of the policy scopes; identifying, based on the hierarchical representation of the policy scopes, one or more policies governing traffic flow between devices associated with each of the policy scopes; and managing application of the one or more policies at the devices.

Abstract: Detecting anomalous behavior of a device, including: generating, using information describing historical activity associated with a user device, a trained model for detecting normal activity for the user device; gathering information describing current activity associated with the user device; and determining, by using the information describing current activity associated with the user device as input to the trained model, whether the user device has deviated from normal activity.

Abstract: Generating user-specific polygraphs for network activity, including: gathering information describing network activity associated with a user and generating, based on the information, a user-specific polygraph that includes one or more destinations associated with the network activity.

Abstract: Disclosed herein are methods, systems, and non-transitory computer-readable storage media for scoring network segmentation policies in order to determine their effectiveness before, during and after enforcement. In one aspect, a method includes identifying one or more applications within an enterprise network; identifying at least one network security policy in association with the one or more applications within the enterprise network; determining a score of the network security policy based on information corresponding to exposure of each of the one or more applications within the enterprise network; and executing the network security policy based on the score.

Abstract: Detecting anomalous behavior of a device, including: generating, using information describing historical activity associated with a user device, a trained model for detecting normal activity for the user device; gathering information describing current activity associated with the user device; and determining, by using the information describing current activity associated with the user device as input to the trained model, whether the user device has deviated from normal activity.

Abstract: Detecting deviations from typical user behavior, including: identifying a geographic location of a device that is associated with a user; determining device activity associated with the user; and detecting, based on a profile associated with the user, that the device activity associated with the user deviates from normal activity for the user.

Abstract: Establishing a location profile for a user device, including: gathering information associated with the location of a user device; determining, based on the information associated with the location of a user device, whether the user device is being accessed at a known location; responsive to determining that the user device is being accessed at a known location: determining a characterization of the known location; and determining, based on the characterization of the known location, whether device utilization is anomalous; and responsive to determining that the user device is not being accessed at a known location: determining a characterization of the unknown location; and determining, based on the characterization of the unknown location, whether device utilization is anomalous.

Abstract: The present disclosure relates to methods, systems, and non-transitory computer readable media for discovering policy scopes within an enterprise network and managing network policies for discovered policy scopes. In one aspect, a method includes identifying one or more communities of devices in an enterprise network; defining, from the one or more communities of devices, policy scopes in the enterprise network; generating a hierarchical representation of the policy scopes; identifying, based on the hierarchical representation of the policy scopes, one or more policies governing traffic flow between devices associated with each of the policy scopes; and managing application of the one or more policies at the devices.

Abstract: Disclosed herein are methods, systems, and non-transitory computer-readable storage media for scoring network segmentation policies in order to determine their effectiveness before, during and after enforcement.

Abstract: A application and network analytics platform can capture telemetry (e.g., flow data, server data, process data, user data, policy data, etc.) within a network. The application and network analytics platform can determine flows between servers (physical and virtual servers), server configuration information, and the processes that generated the flows from the telemetry. The application and network analytics platform can compute feature vectors for the processes. The application and network analytics platform can utilize the feature vectors to assess various degrees of functional similarity among the processes. These relationships can form a hierarchical graph providing different application perspectives, from a coarse representation in which the entire data center can be a “root application” to a fine representation in which it may be possible to view the individual processes running on each server.

Abstract: Techniques, devices, systems, and protocols are disclosed herein that relate to data transfer between communication nodes via multiple heterogeneous paths. In various embodiments, network coding may be used to improve data flow and reliability in a multiple path scenario. Transmission control protocol (TCP) may also be used within different paths to further enhance data transfer reliability. In some embodiments, multiple levels of network coding may be provided within a transmitter in a multiple path scenario, with one level being applied across all paths and another being applied within individual paths.

Abstract: Techniques, devices, systems, and protocols are disclosed herein that relate to data transfer between communication nodes via multiple heterogeneous paths. In various embodiments, network coding may be used to improve data flow and reliability in a multiple path scenario. Transmission control protocol (TCP) may also be used within different paths to further enhance data transfer reliability. In some embodiments, multiple levels of network coding may be provided within a transmitter in a multiple path scenario, with one level being applied across all paths and another being applied within individual paths.