Adaptive observation of behavioral features on a mobile device
First Claim
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1. A method for behavior tracking in a mobile device using behavioral vectors, the method comprising:
- determining in a processor of the mobile device a feature that is to be observed in the mobile device in order to identify a suspicious mobile device behavior,adaptively observing the determined feature in the mobile device by collecting behavior information from an instrumented component that is associated with the determined feature;
generating a vector data structure that describes the collected behavior information via a plurality of numbers; and
performing behavior analysis operations that include applying a classifier model that includes decision nodes to the vector data structure to identify the suspicious mobile device behavior.
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Abstract
Methods, devices and systems for detecting suspicious or performance-degrading mobile device behaviors intelligently, dynamically, and/or adaptively determine computing device behaviors that are to be observed, the number of behaviors that are to be observed, and the level of detail or granularity at which the mobile device behaviors are to be observed. The various aspects efficiently identify suspicious or performance-degrading mobile device behaviors without requiring an excessive amount of processing, memory, or energy resources.
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Citations
20 Claims
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1. A method for behavior tracking in a mobile device using behavioral vectors, the method comprising:
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determining in a processor of the mobile device a feature that is to be observed in the mobile device in order to identify a suspicious mobile device behavior, adaptively observing the determined feature in the mobile device by collecting behavior information from an instrumented component that is associated with the determined feature; generating a vector data structure that describes the collected behavior information via a plurality of numbers; and performing behavior analysis operations that include applying a classifier model that includes decision nodes to the vector data structure to identify the suspicious mobile device behavior. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18)
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19. A mobile computing device, comprising:
a processor configured with processor-executable instructions to perform operations comprising; determining a feature that is to be observed in the mobile computing device in order to identify a suspicious mobile device behavior, and adaptively observing the determined feature in the mobile computing device by collecting behavior information from an instrumented component that is associated with the determined feature; generating a vector data structure that describes the collected behavior information via a plurality of numbers; and performing behavior analysis operations that include applying a classifier model that includes decision nodes to the vector data structure to identify the suspicious mobile device behavior.
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20. A non-transitory processor-readable storage medium having stored thereon processor-executable instructions configured to cause a processor of a mobile computing device to perform operations comprising:
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determining a feature that is to be observed in the mobile computing device in order to identify a suspicious mobile device behavior, and adaptively observing the determined feature in the mobile computing device by collecting behavior information from an instrumented component that is associated with the determined feature; generating a vector data structure that describes the collected behavior information via a plurality of numbers; and performing behavior analysis operations that include applying a classifier model that includes decision nodes to the vector data structure to identify the suspicious mobile device behavior.
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Specification