Abstract: Zero trust and micro-segmentation techniques may be collectively used to enhance network security. To establish, refine, and enforce a zero-trust least-privileged policy, the network may be segmented to put each device of the network into a respective network of one, which forces all network traffic to pass through a zero-trust gatekeeper. The gatekeeper may then monitor and analyze the traffic to establish, refine, and enforce the zero-trust least-privileged policy, which reduces network access to only a limited set of network actions and/or paths. Using the gatekeeper, network traffic may be monitored to progressively establish the policy as well as to continually refine the policy. Recommended actions may be determined based on the analysis of the monitored network traffic and provided to the user to allow user feedback on the communication rules of zero-trust policy.

Abstract: Zero trust and micro-segmentation techniques may be collectively used to enhance network security. To establish, refine, and enforce a zero-trust least-privileged policy, the network may be segmented to put each device of the network into a respective network of one, which forces all network traffic to pass through a zero-trust gatekeeper. The gatekeeper may then monitor and analyze the traffic to establish, refine, and enforce the zero-trust least-privileged policy, which reduces network access to only a limited set of network actions and/or paths. Using the gatekeeper, network traffic may be monitored to progressively establish the policy as well as to continually refine the policy. Recommended actions may be determined based on the analysis of the monitored network traffic and provided to the user to allow user feedback on the communication rules of zero-trust policy.

Abstract: Zero trust and micro-segmentation techniques may be collectively used to enhance network security. To establish, refine, and enforce a zero-trust least-privileged policy, the network may be segmented to put each device of the network into a respective network of one, which forces all network traffic to pass through a zero-trust gatekeeper. The gatekeeper may then monitor and analyze the traffic to establish, refine, and enforce the zero-trust least-privileged policy, which reduces network access to only a limited set of network actions and/or paths. Using the gatekeeper, network traffic may be monitored to progressively establish the policy as well as to continually refine the policy. Recommended actions may be determined based on the analysis of the monitored network traffic and provided to the user to allow user feedback on the communication rules of zero-trust policy.

Abstract: Zero trust and micro-segmentation techniques may be collectively used to enhance network security. To establish, refine, and enforce a zero-trust least-privileged policy, the network may be segmented to put each device of the network into a respective network of one, which forces all network traffic to pass through a zero-trust gatekeeper. The gatekeeper may then monitor and analyze the traffic to establish, refine, and enforce the zero-trust least-privileged policy, which reduces network access to only a limited set of network actions and/or paths. Using the gatekeeper, network traffic may be monitored to progressively establish the policy as well as to continually refine the policy. Recommended actions may be determined based on the analysis of the monitored network traffic and provided to the user to allow user feedback on the communication rules of zero-trust policy.

Abstract: Zero trust and micro-segmentation techniques may be collectively used to enhance network security. To establish, refine, and enforce a zero-trust least-privileged policy, the network may be segmented to put each device of the network into a respective network of one, which forces all network traffic to pass through a zero-trust gatekeeper. The gatekeeper may then monitor and analyze the traffic to establish, refine, and enforce the zero-trust least-privileged policy, which reduces network access to only a limited set of network actions and/or paths. Using the gatekeeper, network traffic may be monitored to progressively establish the policy as well as to continually refine the policy. Recommended actions may be determined based on the analysis of the monitored network traffic and provided to the user to allow user feedback on the communication rules of zero-trust policy.

Abstract: Zero trust and micro-segmentation techniques may be collectively used to enhance network security. To establish, refine, and enforce a zero-trust least-privileged policy, the network may be segmented to put each device of the network into a respective network of one, which forces all network traffic to pass through a zero-trust gatekeeper. The gatekeeper may then monitor and analyze the traffic to establish, refine, and enforce the zero-trust least-privileged policy, which reduces network access to only a limited set of network actions and/or paths. Using the gatekeeper, network traffic may be monitored to progressively establish the policy as well as to continually refine the policy. Recommended actions may be determined based on the analysis of the monitored network traffic and provided to the user to allow user feedback on the communication rules of zero-trust policy.

Abstract: Zero trust and micro-segmentation techniques may be collectively used to enhance network security. To establish, refine, and enforce a zero-trust least-privileged policy, the network may be segmented to put each device of the network into a respective network of one, which forces all network traffic to pass through a zero-trust gatekeeper. The gatekeeper may then monitor and analyze the traffic to establish, refine, and enforce the zero-trust least-privileged policy, which reduces network access to only a limited set of network actions and/or paths. Using the gatekeeper, network traffic may be monitored to progressively establish the policy as well as to continually refine the policy. Recommended actions may be determined based on the analysis of the monitored network traffic and provided to the user to allow user feedback on the communication rules of zero-trust policy.

Abstract: Zero trust and micro-segmentation techniques may be collectively used to enhance network security. To establish, refine, and enforce a zero-trust least-privileged policy, the network may be segmented to put each device of the network into a respective network of one, which forces all network traffic to pass through a zero-trust gatekeeper. The gatekeeper may then monitor and analyze the traffic to establish, refine, and enforce the zero-trust least-privileged policy, which reduces network access to only a limited set of network actions and/or paths. Using the gatekeeper, network traffic may be monitored to progressively establish the policy as well as to continually refine the policy. Recommended actions may be determined based on the analysis of the monitored network traffic and provided to the user to allow user feedback on the communication rules of zero-trust policy.

Abstract: Zero trust and micro-segmentation techniques may be collectively used to enhance network security. To establish, refine, and enforce a zero-trust least-privileged policy, the network may be segmented to put each device of the network into a respective network of one, which forces all network traffic to pass through a zero-trust gatekeeper. The gatekeeper may then monitor and analyze the traffic to establish, refine, and enforce the zero-trust least-privileged policy, which reduces network access to only a limited set of network actions and/or paths. Using the gatekeeper, network traffic may be monitored to progressively establish the policy as well as to continually refine the policy. Recommended actions may be determined based on the analysis of the monitored network traffic and provided to the user to allow user feedback on the communication rules of zero-trust policy.

Abstract: The present disclosure relates to identifying process flows from log sources (e.g., log files), and generating visual representations (e.g., flow diagrams, Sankey diagrams, etc.) of the identified process flows. In addition, the present disclosure relates to clustering of tree structures based on the shape of the tree structure using one or more hashing algorithms. The present disclosure also relates to a user interface that presents a query builder for efficiently querying a log analytics system for tree structures that satisfy a user-defined range.

Abstract: The present disclosure relates to identifying process flows from log sources (e.g., log files), and generating visual representations (e.g., flow diagrams, Sankey diagrams, etc.) of the identified process flows. In addition, the present disclosure relates to clustering of tree structures based on the shape of the tree structure using one or more hashing algorithms. The present disclosure also relates to a user interface that presents a query builder for efficiently querying a log analytics system for tree structures that satisfy a user-defined range.

Abstract: Disclosed is a system, method, and computer program product for implementing a log analytics method and system that can configure, collect, and analyze log records in an efficient manner. In addition, a method and system is provided for implementing collection-wise processing within a log analytics system.

Abstract: The present disclosure relates to querying log messages stored at log endpoints (e.g., log cores) using query strings that reference virtual fields, and generating log field views presenting aggregate information for groups of log messages that are grouped based on criteria defined in the query string.

Abstract: Some embodiments relate to assigning individual log messages to clusters. An initial cluster assignment may be performed by applying a hash function to one or more non-variable components of the message to generate an initial cluster identifier. Subsequently, clustering may be further refined (e.g., by determining whether to merge clusters based on similarity values). An interface can present a representative message of each cluster and indicate which portions of the message correspond to a variable component. Particular inputs detected at the input corresponding to one of these components can cause other values for the component to be presented. For a given cluster, timestamps of assigned messages can be used to generate a time series, which can facilitate grouping of clusters (with similar or complementary shapes) and/or triggering alerts (with a condition corresponding to a temporal aspect).

Abstract: Some embodiments relate to assigning individual log messages to clusters. An initial cluster assignment may be performed by applying a hash function to one or more non-variable components of the message to generate an initial cluster identifier. Subsequently, clustering may be further refined (e.g., by determining whether to merge clusters based on similarity values). An interface can present a representative message of each cluster and indicate which portions of the message correspond to a variable component. Particular inputs detected at the input corresponding to one of these components can cause other values for the component to be presented. For a given cluster, timestamps of assigned messages can be used to generate a time series, which can facilitate grouping of clusters (with similar or complementary shapes) and/or triggering alerts (with a condition corresponding to a temporal aspect).

Abstract: Some embodiments relate to assigning individual log messages to clusters. An initial cluster assignment may be performed by applying a hash function to one or more non-variable components of the message to generate an initial cluster identifier. Subsequently, clustering may be further refined (e.g., by determining whether to merge clusters based on similarity values). An interface can present a representative message of each cluster and indicate which portions of the message correspond to a variable component. Particular inputs detected at the input corresponding to one of these components can cause other values for the component to be presented. For a given cluster, timestamps of assigned messages can be used to generate a time series, which can facilitate grouping of clusters (with similar or complementary shapes) and/or triggering alerts (with a condition corresponding to a temporal aspect).

Abstract: The present disclosure relates to querying log messages stored at log endpoints (e.g., log cores) using query strings that reference virtual fields, and generating log field views presenting aggregate information for groups of log messages that are grouped based on criteria defined in the query string.

Abstract: The present disclosure relates to identifying process flows from log sources (e.g., log files), and generating visual representations (e.g., flow diagrams, Sankey diagrams, etc.) of the identified process flows. In addition, the present disclosure relates to clustering of tree structures based on the shape of the tree structure using one or more hashing algorithms. The present disclosure also relates to a user interface that presents a query builder for efficiently querying a log analytics system for tree structures that satisfy a user-defined range.

Abstract: The present disclosure relates to identifying process flows from log sources (e.g., log files), and generating visual representations (e.g., flow diagrams, Sankey diagrams, etc.) of the identified process flows. In addition, the present disclosure relates to clustering of tree structures based on the shape of the tree structure using one or more hashing algorithms. The present disclosure also relates to a user interface that presents a query builder for efficiently querying a log analytics system for tree structures that satisfy a user-defined range.

Abstract: Some embodiments relate to assigning individual log messages to clusters. An initial cluster assignment may be performed by applying a hash function to one or more non-variable components of the message to generate an initial cluster identifier. Subsequently, clustering may be further refined (e.g., by determining whether to merge clusters based on similarity values). An interface can present a representative message of each cluster and indicate which portions of the message correspond to a variable component. Particular inputs detected at the input corresponding to one of these components can cause other values for the component to be presented. For a given cluster, timestamps of assigned messages can be used to generate a time series, which can facilitate grouping of clusters (with similar or complementary shapes) and/or triggering alerts (with a condition corresponding to a temporal aspect).