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US Patent 10970395 Security threat monitoring for a storage system

Patent 10970395 was granted and assigned to Pure Storage on April, 2021 by the United States Patent and Trademark Office.

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Contents

Is a
Patent
Patent

Patent attributes

Patent Applicant
Pure Storage
Pure Storage
Current Assignee
Pure Storage
Pure Storage
Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
Patent Number
10970395
Date of Patent
April 6, 2021
Patent Application Number
16168224
Date Filed
October 23, 2018
Patent Citations
‌
US Patent 10042697 Automatic anomaly detection and resolution system
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US Patent 10038707 Rarity analysis in network security anomaly/threat detection
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US Patent 10078473 Resource allocation in networked storage systems
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US Patent 10120887 Demand-driven initialization of data storage structures
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US Patent 10169601 System and method for reading and writing to big data storage formats
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US Patent 10003605 Detection of clustering in graphs in network security analysis
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US Patent 10015177 Lateral movement detection for network security analysis
Patent Citations Received
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US Patent 12061971 Predictive maintenance of automotive engines
0
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US Patent 11531339 Monitoring of drive by wire sensors in vehicles
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US Patent 11586194 Storage and access of neural network models of automotive predictive maintenance
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US Patent 11870490 Optical link fault identification method, apparatus and system
0
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US Patent 11968221 Dynamically federated data breach detection
0
‌
US Patent 11498388 Intelligent climate control in vehicles
‌
US Patent 11586734 Systems and methods for protecting SSDs against threats
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US Patent 11586943 Storage and access of neural network inputs in automotive predictive maintenance
...
Patent Primary Examiner
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Ali S Abyaneh
Patent abstract

An exemplary security threat monitoring system receives performance metric data representative of a performance metric for a storage system, applies the performance metric data as an input to an unsupervised machine learning model, and identifies, based on an output of the unsupervised machine learning model, an anomaly in the performance metric data.

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