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Automated detection of malware using trained neural network-based file classifiers and machine learning

  • US 10,616,252 B2
  • Filed: 06/30/2017
  • Issued: 04/07/2020
  • Est. Priority Date: 06/30/2017
  • Status: Active Grant
First Claim
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1. A computing device comprising:

  • a memory configured to store instructions; and

    a processor configured to execute the instructions from the memory to perform operations comprising;

    generating zero-skip n-gram data for a first subset of files of multiple files included in an application file package, first zero-skip n-gram data of the zero-skip n-gram data indicating occurrences of adjacent characters in printable characters representing a first file of the first subset of files;

    generating skip n-gram data for the first subset of files, first skip n-gram data of the skip n-gram data indicating occurrences of non-adjacent characters in the printable characters representing the first file;

    generating n-gram data for the first subset of files, first n-gram data of the n-gram data indicating occurrences of groups of entropy indicators in a first set of entropy indicators derived from first file entropy data for the first file, each entropy indicator of the first set of entropy indicators having a value representing entropy of a corresponding chunk of the first file;

    generating a first feature vector based on the zero-skip n-gram data, the skip n-gram data, and the n-gram data;

    generating a second feature vector based on occurrences of attributes in a second subset of files of the multiple files;

    sending the first feature vector and the second feature vector to a second computing device as inputs to a file classifier; and

    receiving, from the second computing device, classification data associated with the application file package based on the first feature vector and the second feature vector, the classification data indicating whether the application file package includes malware.

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