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US Patent 9965627 Labeling objects on an endpoint for encryption management

Patent 9965627 was granted and assigned to Sophos Group PLC on May, 2018 by the United States Patent and Trademark Office.

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Is a
Patent
Patent

Patent attributes

Patent Applicant
Sophos Group PLC
Sophos Group PLC
Current Assignee
Sophos Group PLC
Sophos Group PLC
Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
Patent Number
9965627
Patent Inventor Names
John Edward Tyrone Shaw0
Andrew J. Thomas0
Anthony John Merry0
Daniel Salvatore Schiappa0
Harald Schütz0
Kenneth D. Ray0
Mark D. Harris0
Neil Robert Tyndale Watkiss0
...
Date of Patent
May 8, 2018
Patent Application Number
14485769
Date Filed
September 14, 2014
Patent Citations Received
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US Patent 11928631 Threat detection with business impact scoring
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US Patent 12061677 Secure processor for detecting and preventing exploits of software vulnerability
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US Patent 12079335 System context database management
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US Patent 12079757 Endpoint with remotely programmable data recorder
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US Patent 12137123 Rapid predictive analysis of very large data sets using the distributed computational graph
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US Patent 12130923 Methods and apparatus for augmenting training data using large language models
0
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US Patent 11755974 Computer augmented threat evaluation
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US Patent 11836664 Enterprise network threat detection
0
...
Patent Primary Examiner
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Hien L Duong
Patent abstract

Threat detection instrumentation is simplified by providing and updating labels for computing objects in a context-sensitive manner. This may include simple labeling schemes to distinguish between objects, e.g., trusted/untrusted processes or corporate/private data. This may also include more granular labeling schemes such as a three-tiered scheme that identifies a category (e.g., financial, e-mail, game), static threat detection attributes (e.g., signatures, hashes, API calls), and explicit identification (e.g., what a file or process calls itself). By tracking such data for various computing objects and correlating these labels to malware occurrences, rules can be written for distribution to endpoints to facilitate threat detection based on, e.g., interactions of labeled objects, changes to object labels, and so forth. In this manner, threat detection based on complex interactions of computing objects can be characterized in a platform independent manner and pre-processed on endpoints without requiring significant communications overhead with a remote threat management facility.

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