EVENT MINI-GRAPHS IN DATA INTAKE STAGE OF MACHINE DATA PROCESSING PLATFORM
First Claim
1. A method for processing data at data intake for detection of an anomaly in a distributed computer environment, the method comprising:
- receiving event data representing an event on a computer network, the event data being indicative of a plurality of entities and an action involved in the event;
identifying the entities and a relationship between the entities, based on the action in the event data;
creating, for the event, a record of the relationship between the entities by using a data structure representing a relationship graph, the relationship graph including at least two nodes and an edge between the two nodes, each node representing one of the entities, the edge representing the relationship between the entities; and
before sending the event data to a processing fabric for performing anomaly detection, updating the event data representing the event to include the record of the relationship,wherein the record of the relationship is specific to the event, andwherein the anomaly detection is performed based on applying a machine learning model to perform analytics on at least a portion of a composite relationship graph that is combined from relationship graphs for a plurality of events.
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Accused Products
Abstract
A security platform employs a variety techniques and mechanisms to detect security related anomalies and threats in a computer network environment. The security platform is “big data” driven and employs machine learning to perform security analytics. The security platform performs user/entity behavioral analytics (UEBA) to detect the security related anomalies and threats, regardless of whether such anomalies/threats were previously known. The security platform can include both real-time and batch paths/modes for detecting anomalies and threats. By visually presenting analytical results scored with risk ratings and supporting evidence, the security platform enables network security administrators to respond to a detected anomaly or threat, and to take action promptly.
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Citations
31 Claims
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1. A method for processing data at data intake for detection of an anomaly in a distributed computer environment, the method comprising:
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receiving event data representing an event on a computer network, the event data being indicative of a plurality of entities and an action involved in the event; identifying the entities and a relationship between the entities, based on the action in the event data; creating, for the event, a record of the relationship between the entities by using a data structure representing a relationship graph, the relationship graph including at least two nodes and an edge between the two nodes, each node representing one of the entities, the edge representing the relationship between the entities; and before sending the event data to a processing fabric for performing anomaly detection, updating the event data representing the event to include the record of the relationship, wherein the record of the relationship is specific to the event, and wherein the anomaly detection is performed based on applying a machine learning model to perform analytics on at least a portion of a composite relationship graph that is combined from relationship graphs for a plurality of events. - View Dependent Claims (2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 31)
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4. (canceled)
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29. A computer system for detection of an anomaly in a distributed computer environment, the system comprising:
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a communication device; and a processor configured to; receive, via the communication device, event data representing an event on a computer network, the event data being indicative of a plurality of entities and an action involved in the event; identify the entities and a relationship between the entities, based on the action in the event data; create, for the event, a record of the relationship between the entities by using a data structure representing a relationship graph, the relationship graph including at least two nodes and an edge between the two nodes, each node representing one of the entities, the edge representing the relationship between the entities; and before sending the event data to a processing fabric for performing anomaly detection, update the event data representing the event to include the record of the relationship, wherein the record of the relationship is specific to the event, and wherein the anomaly detection is performed based on applying a machine learning model to perform analytics on at least a portion of a composite relationship graph that is combined from relationship graphs for a plurality of events.
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30. A non-transitory machine-readable storage medium for use in a processing system for detection of an anomaly in a distributed computer environment, the non-transitory machine-readable storage medium storing instructions, an execution of which in the processing system causes the processing system to perform operations comprising:
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receiving event data representing an event on a computer network, the event data being indicative of a plurality of entities and an action involved in the event; identifying the entities and a relationship between the entities, based on the action in the event data; creating, for the event, a record of the relationship between the entities by using a data structure representing a relationship graph, the relationship graph including at least two nodes and an edge between the two nodes, each node representing one of the entities, the edge representing the relationship between the entities; and before sending the event data to a processing fabric for performing anomaly detection, updating the event data representing the event to include the record of the relationship, wherein the record of the relationship is specific to the event, and wherein the anomaly detection is performed based on applying a machine learning model to perform analytics on at least a portion of a composite relationship graph that is combined from relationship graphs for a plurality of events.
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Specification