Methods and systems of using application-specific and application-type-specific models for the efficient classification of mobile device behaviors
First Claim
1. A method of generating and using classifier models in a mobile device based on application types having distinct characteristics, comprising:
- receiving, in a processor of the mobile device, a finite state machine that includes information that is suitable for conversion into a plurality of boosted decision stumps, wherein each of the plurality of boosted decision stumps evaluate one of a plurality of test conditions;
converting the information included in the received finite state machine into the plurality of boosted decision stumps that each evaluate one of the plurality of test conditions;
generating a family of lean classifier models in the mobile device based on the plurality of boosted decision stumps;
generating, via the processor of the mobile device, an application-type-specific classifier model that includes and prioritizes boosted decision stumps in the plurality of boosted decision stumps that evaluate a subset of test conditions in the plurality of test conditions, wherein the subset of test conditions are determined to evaluate mobile device features that are used by one type of software application, wherein the one type of software application is determined to be suitable for executing on the mobile device;
selecting a lean classifier model from the family of lean classifier models; and
applying collected behavior information to the generated application-type-specific classifier model and the selected lean classifier model in parallel to classify a behavior of the mobile device.
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Abstract
Methods, and mobile devices implementing the methods, use application-specific and/or application-type specific classifier to improve the efficiency and performance of a comprehensive behavioral monitoring and analysis system predicting whether a software application is causing undesirable or performance depredating behavior. The application-specific and application-type specific classifier models may include a reduced and more focused subset of the decision nodes that are included in a full or more complete classifier model that may be received or generated in the mobile device. The locally generated application-specific and/or application-type specific classifier models may be used to perform real-time behavior monitoring and analysis operations by applying the application-based classifier models to a behavior/feature vector generated by monitoring mobile device behavior. The various aspects focus monitoring and analysis operations on a small number of features that are most important for determining whether operations of a software application are contributing to undesirable or performance depredating behavior.
32 Citations
16 Claims
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1. A method of generating and using classifier models in a mobile device based on application types having distinct characteristics, comprising:
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receiving, in a processor of the mobile device, a finite state machine that includes information that is suitable for conversion into a plurality of boosted decision stumps, wherein each of the plurality of boosted decision stumps evaluate one of a plurality of test conditions; converting the information included in the received finite state machine into the plurality of boosted decision stumps that each evaluate one of the plurality of test conditions; generating a family of lean classifier models in the mobile device based on the plurality of boosted decision stumps; generating, via the processor of the mobile device, an application-type-specific classifier model that includes and prioritizes boosted decision stumps in the plurality of boosted decision stumps that evaluate a subset of test conditions in the plurality of test conditions, wherein the subset of test conditions are determined to evaluate mobile device features that are used by one type of software application, wherein the one type of software application is determined to be suitable for executing on the mobile device; selecting a lean classifier model from the family of lean classifier models; and applying collected behavior information to the generated application-type-specific classifier model and the selected lean classifier model in parallel to classify a behavior of the mobile device. - View Dependent Claims (2, 3, 4, 5, 6)
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7. A mobile computing device, comprising:
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a memory; and a processor coupled to the memory, wherein the processor is configured with processor-executable instructions to perform operations comprising; receiving a finite state machine that includes information that is suitable for conversion into a plurality of boosted decision stumps, wherein each of the plurality of boosted decision stumps evaluate one of a plurality of test conditions; converting the information included in the received finite state machine into the plurality of boosted decision stumps that each evaluate one of the plurality of test conditions; generating a family of lean classifier models based on the plurality of boosted decision stumps; generating an application-type-specific classifier model that includes and prioritizes boosted decision stumps in the plurality of boosted decision stumps that evaluate a subset of test conditions in the plurality of test conditions, wherein the subset of test conditions are determined to evaluate mobile device features that are used by one type of software application, wherein the one type of software application is determined to be suitable for executing on the mobile device; selecting a lean classifier model from the family of lean classifier models; and applying collected behavior information to the generated application-type-specific classifier model and the selected lean classifier model in parallel to classify a behavior of the mobile computing device. - View Dependent Claims (8, 9)
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10. A non-transitory computer readable storage medium having stored thereon processor-executable software instructions configured to cause a processor of a mobile device to perform operations comprising:
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receiving a finite state machine that includes information that is suitable for conversion into a plurality of boosted decision stumps, wherein each of the plurality of boosted decision stumps evaluate one of a plurality of test conditions; converting the information included in the received finite state machine into the plurality of boosted decision stumps that each evaluate one of the plurality of test conditions; generating a family of lean classifier models in the mobile device based on the plurality of boosted decision stumps; generating an application-type-specific classifier model that includes and prioritizes boosted decision stumps in the plurality of boosted decision stumps that evaluate a subset of test conditions in the plurality of test conditions, wherein the subset of test conditions are determined to evaluate mobile device features that are used by one type of software application wherein the one type of software application is determined to be suitable for executing on the mobile device; selecting a lean classifier model from the family of lean classifier models; and applying collected behavior information to the generated application-type-specific classifier model and the selected lean classifier model in parallel to classify a behavior of the mobile device. - View Dependent Claims (11, 12)
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13. A mobile computing device, comprising:
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means for receiving a finite state machine that includes information that is suitable for conversion into a plurality of boosted decision stumps, wherein each of the plurality of boosted decision stumps evaluate one of a plurality of test conditions; means for converting the information included in the received finite state machine into the plurality of boosted decision stumps that each evaluate one of the plurality of test conditions; means for generating a family of lean classifier models based on the plurality of boosted decision stumps; means for generating an application-type-specific classifier model that includes and prioritizes boosted decision stumps in the plurality of boosted decision stumps that evaluate a subset of test conditions in the plurality of test conditions, wherein the subset of test conditions are determined to evaluate mobile device features that are used by one type of software application, wherein the one type of software application is determined to be suitable for executing on the mobile device; means for selecting a lean classifier model from the family of lean classifier models; and means for applying collected behavior information to the generated application-type-specific classifier model and the selected lean classifier model in parallel to classify a behavior of the mobile computing device. - View Dependent Claims (14, 15, 16)
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Specification