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In-app behavior-based attack dectection

  • US 10,182,063 B2
  • Filed: 03/02/2016
  • Issued: 01/15/2019
  • Est. Priority Date: 03/02/2016
  • Status: Active Grant
First Claim
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1. A method comprising:

  • capturing events within an application running on a hardware computing device, wherein one or more of the events have corresponding inputs received from one or more sources external to the application;

    generating, with a behavior detection agent, an event stream from the captured events where each captured event has a corresponding feature vector, wherein the feature vectors are based on binary feature vectors of training data;

    analyzing, with the behavior detection agent, the feature vectors of the event stream for feature frequencies and associations corresponding to one or more previously generated attack profiles;

    generating, from the analysis of the feature vectors of the event stream, at least one sub-optimal function having feature detection rules ranked according to satisfiability rates; and

    initiating, with the behavior detection agent, an attack response in response to finding one or more significant feature frequencies and associations based on the at least one sub-optimal function, wherein the attack response comprises at least changing an operational configuration of the application.

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