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Methods and apparatus for analyzing sequences of application programming interface traffic to identify potential malicious actions

  • US 10,699,010 B2
  • Filed: 10/12/2018
  • Issued: 06/30/2020
  • Est. Priority Date: 10/13/2017
  • Status: Active Grant
First Claim
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1. A non-transitory processor-readable medium storing code representing instructions to be executed by a processor, the code comprising code to cause the processor to:

  • receive, from a client device, a set of application programming interface (API) calls having a sequence;

    provide an indication associated with at least one API call from the set of API calls as an input to a machine learning model to identify a predicted sequence of API calls associated with the at least one API call;

    calculate a plurality of consistency scores for each pair of API calls from the set of API calls by comparing (1) a proximity within the sequence of a first API call in that pair of API calls to a second API call in that pair of API calls and (2) a proximity within the predicted sequence of the first API call in that pair of API calls to the second API call in that pair of API calls, each consistency score from the plurality of consistency scores for each pair of API calls from the set of API calls being associated with a predetermined context;

    generate a combined consistency score for each pair of API calls from the set of API calls by combining each consistency score from the plurality of consistency scores for that pair of API calls with the remaining consistency scores from the plurality of consistency scores for that pair of API calls;

    identify, in response to determining that the combined consistency score for at least one pair of API calls from the set of API calls is below a predetermined threshold, that the client device is operating in a malicious manner; and

    restrict API calls received from the client device based on identifying that the client device is operating in the malicious manner.

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