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US Patent 9690938 Methods and apparatus for machine learning based malware detection

Patent 9690938 was granted and assigned to Invincea, Inc. on June, 2017 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
9690938
Date of Patent
June 27, 2017
Patent Application Number
15228728
Date Filed
August 4, 2016
Patent Citations Received
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US Patent 12135789 Systems and methods of attack type and likelihood prediction
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US Patent 12079757 Endpoint with remotely programmable data recorder
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US Patent 12111933 System and method for dynamically updating existing threat models based on newly identified active threats
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US Patent 12130923 Methods and apparatus for augmenting training data using large language models
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US Patent 11711380 Systems and methods for parallel virus and malware scan between agents in a cloud environment
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US Patent 11711388 Automated detection of malware using trained neural network-based file classifiers and machine learning
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US Patent 11714908 Bit-level data generation and artificial intelligence techniques and architectures for data protection
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US Patent 11755974 Computer augmented threat evaluation
...
Patent Primary Examiner
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Brandon 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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