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US Patent 9225730 Graph based detection of anomalous activity

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Is a
Patent
Patent

Patent attributes

Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
Patent Number
9225730
Date of Patent
December 29, 2015
Patent Application Number
14219819
Date Filed
March 19, 2014
Patent Citations Received
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US Patent 12130878 Deduplication of monitored communications data in a cloud environment
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US Patent 12120140 Detecting threats against computing resources based on user behavior changes
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US Patent 12126643 Leveraging generative artificial intelligence (‘AI’) for securing a monitored deployment
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US Patent 12126695 Enhancing security of a cloud deployment based on learnings from other cloud deployments
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US Patent 11695828 System and method for peer group detection, visualization and analysis in identity management artificial intelligence systems using cluster based analysis of network identity graphs
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US Patent 11693958 Processing and storing event data in a knowledge graph format for anomaly detection
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US Patent 11710078 System and method for incremental training of machine learning models in artificial intelligence systems, including incremental training using analysis of network identity graphs
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US Patent 11765249 Facilitating developer efficiency and application quality
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Patent Primary Examiner
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Jason K Gee
Patent abstract

Techniques are described for graph-based analysis of event data in a computing environment. Event data is collected from host devices, the event data describing events in which devices, processes, or services are accessed in the environment. The event data is arranged into a graph that includes vertices corresponding to devices, processes, or services, and edges that connect pairs of vertices. Each edge may identify an event by connecting two vertices corresponding to two devices, processes, or services included in the event. A rarity metric is determined for each edge, indicating a rarity of events of a particular type connecting two vertices. A risk metric may also be determined for each edge, indicating a security risk associated with the event type or the target of the event. The graph may be traversed according to the risk and rarity metrics, to identify patterns of anomalous activity in the event data.

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