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US Patent 10896256 Methods and apparatus for machine learning based malware detection

Patent 10896256 was granted and assigned to Invincea, Inc. on January, 2021 by the United States Patent and Trademark Office.

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Contents

Is a
Patent
Patent

Patent attributes

Patent Applicant
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Invincea, Inc.
Current Assignee
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Invincea, Inc.
Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
Patent Number
10896256
Date of Patent
January 19, 2021
Patent Application Number
16415471
Date Filed
May 17, 2019
Patent Citations
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US Patent 10318735 Methods and apparatus for detecting whether a string of characters represents malicious activity using machine learning
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US Patent 10649970 Methods and apparatus for detection of functionality
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US Patent 10104100 Systems and methods for detecting anomalies that are potentially indicative of malicious attacks
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US Patent 10303875 Methods and apparatus for machine learning based malware detection
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US Patent 10474818 Methods and devices for detection of malware
Patent Citations Received
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US Patent 11936665 Method for monitoring data transiting via a user equipment
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US Patent 11853427 Methods and apparatus for detecting whether a string of characters represents malicious activity using machine learning
0
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US Patent 11868471 Particle encoding for automatic sample processing
0
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US Patent 11544380 Methods and apparatus for detecting whether a string of characters represents malicious activity using machine learning
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US Patent 11625483 Fast identification of trustworthy deep neural networks
0
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US Patent 11803642 Optimization of high entropy data particle extraction
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US Patent 11841947 Methods and apparatus for machine learning based malware detection
0
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US Patent 11841954 Systems and methods for automated threat modeling when deploying infrastructure as a code
0
Patent Primary Examiner
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Brandon S Hoffman
Patent abstract

Apparatus and methods describe herein, for example, a process that can include receiving a potentially malicious file, and dividing the potentially malicious file into a set of byte windows. The process can include calculating at least one attribute associated with each byte window from the set of byte windows for the potentially malicious file. In such an instance, the at least one attribute is not dependent on an order of bytes in the potentially malicious file. The process can further include identifying a probability that the potentially malicious file is malicious, based at least in part on the at least one attribute and a trained threat model.

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