On-device real-time behavior analyzer
First Claim
1. A method of generating data models in a communication system, comprising:
- applying machine learning techniques to generate a first family of classifier models using a boosted decision tree to describe a corpus of behavior vectors;
computing an exclusive answer ratio for one or more nodes of the boosted decision tree; and
determining which factors in the first family of classifier models have a high probability of enabling a mobile device to conclusively determine whether a mobile device behavior is not benign based on computed exclusive answer ratios.
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Abstract
Methods, systems and devices for generating data models in a communication system may include applying machine learning techniques to generate a first family of classifier models using a boosted decision tree to describe a corpus of behavior vectors. Such behavior vectors may be used to compute a weight value for one or more nodes of the boosted decision tree. Classifier models factors having a high probably of determining whether a mobile device behavior is benign or not benign based on the computed weight values may be identified. Computing weight values for boosted decision tree nodes may include computing an exclusive answer ratio for generated boosted decision tree nodes. The identified factors may be applied to the corpus of behavior vectors to generate a second family of classifier models identifying fewer factors and data points relevant for enabling the mobile device to determine whether a behavior is benign or not benign.
181 Citations
30 Claims
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1. A method of generating data models in a communication system, comprising:
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applying machine learning techniques to generate a first family of classifier models using a boosted decision tree to describe a corpus of behavior vectors; computing an exclusive answer ratio for one or more nodes of the boosted decision tree; and determining which factors in the first family of classifier models have a high probability of enabling a mobile device to conclusively determine whether a mobile device behavior is not benign based on computed exclusive answer ratios. - View Dependent Claims (2, 3, 4)
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5. A communication system, comprising:
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a server comprising; means for applying machine learning techniques to generate a first family of classifier models using a boosted decision tree to describe a corpus of behavior vectors; means for computing an exclusive answer ratio for one or more nodes of the boosted decision tree; means for determining which factors in the first family of classifier models have a high probability of enabling a mobile device to conclusively determine whether a mobile device behavior is not benign based on computed exclusive answer ratios; means for applying the factors to the corpus of behavior vectors to generate a second family of classifier models that identify fewer data points as being relevant for enabling the mobile device to conclusively determine whether the mobile device behavior is not benign; means for generating a mobile device classifier based on the second family of classifier models; and means for sending the generated mobile device classifier to the mobile device; and a mobile computing device, comprising; means for sending behavior vectors to the server; means for receiving the mobile device classifier from the server; and means for classifying a behavior of the mobile computing device based on the received mobile device classifier.
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6. A communication system, comprising:
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a server comprising a server processor configured with server-executable instructions to perform operations comprising; applying machine learning techniques to generate a first family of classifier models using a boosted decision tree to describe a corpus of behavior vectors; computing an exclusive answer ratio for one or more nodes of the boosted decision tree; determining which factors in the first family of classifier models have a high probability of enabling a mobile device to conclusively determine whether a mobile device behavior is not benign based on computed exclusive answer ratios; applying the factors to the corpus of behavior vectors to generate a second family of classifier models that identify fewer data points as being relevant for enabling the mobile device to conclusively determine whether the mobile device behavior is not benign; generating a mobile device classifier based on the second family of classifier models; and sending the generated mobile device classifier to the mobile device; and a mobile computing device comprising a device processor configured with processor-executable instructions to perform operations comprising; sending behavior vectors to the server; receiving the mobile device classifier from the server; and classifying a behavior of the mobile computing device based on the received mobile device classifier.
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7. A server, comprising:
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means for applying machine learning techniques to generate a first family of classifier models using a boosted decision tree to describe a corpus of behavior vectors; means for computing an exclusive answer ratio for one or more nodes of the boosted decision tree; and means for determining which factors in the first family of classifier models have a high probability of enabling a mobile device to conclusively determine whether a mobile device behavior is not benign based on computed exclusive answer ratios. - View Dependent Claims (8, 9)
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10. A server, comprising:
a processor configured with processor-executable instructions to perform operations comprising; applying machine learning techniques to generate a first family of classifier models using a boosted decision tree to describe a corpus of behavior vectors; computing an exclusive answer ratio for one or more nodes of the boosted decision tree; and determining which factors in the first family of classifier models have a high probability of enabling a mobile device to conclusively determine whether a mobile device behavior is not benign based on computed exclusive answer ratios. - View Dependent Claims (11, 12)
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13. A non-transitory computer readable storage medium having stored thereon server-executable software instructions configured to cause a server processor to perform operations for generating data models in a communication system, comprising:
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applying machine learning techniques to generate a first family of classifier models using a boosted decision tree to describe a corpus of behavior vectors; computing an exclusive answer ratio for one or more nodes of the boosted decision tree; and determining which factors in the first family of classifier models have a high probability of enabling a mobile device to conclusively determine whether a mobile device behavior is not benign based on the computed exclusive answer ratios. - View Dependent Claims (14, 15)
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16. A method of generating data models in a communication system, comprising:
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applying machine learning techniques to generate a first family of classifier models using a boosted decision tree to describe a corpus of behavior vectors; computing an answer ratio for one or more nodes of the boosted decision tree; and determining which factors in the first family of classifier models have a high probability of enabling a mobile device to conclusively determine whether a mobile device behavior is not benign based on computed answer ratios. - View Dependent Claims (17, 18, 19)
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20. A communication system, comprising:
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a server comprising; means for applying machine learning techniques to generate a first family of classifier models using a boosted decision tree to describe a corpus of behavior vectors; means for computing an answer ratio for one or more nodes of the boosted decision tree; means for determining which factors in the first family of classifier models have a high probability of enabling a mobile device to conclusively determine whether a mobile device behavior is not benign based on computed answer ratios; means for applying the factors to the corpus of behavior vectors to generate a second family of classifier models that identify fewer data points as being relevant for enabling the mobile device to conclusively determine whether the mobile device behavior is not benign; means for generating a mobile device classifier based on the second family of classifier models; and means for sending the generated mobile device classifier to the mobile device; and a mobile computing device, comprising; means for sending behavior vectors to the server; means for receiving the mobile device classifier from the server; and means for classifying a behavior of the mobile computing device based on the received mobile device classifier.
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21. A communication system, comprising:
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a server comprising a server processor configured with server-executable instructions to perform operations comprising; applying machine learning techniques to generate a first family of classifier models using a boosted decision tree to describe a corpus of behavior vectors; computing an answer ratio for one or more nodes of the boosted decision tree; determining which factors in the first family of classifier models have a high probability of enabling a mobile device to conclusively determine whether a mobile device behavior is not benign based on computed answer ratios; applying the factors to the corpus of behavior vectors to generate a second family of classifier models that identify fewer data points as being relevant for enabling the mobile device to conclusively determine whether the mobile device behavior is not benign; generating a mobile device classifier based on the second family of classifier models; and sending the generated mobile device classifier to the mobile device; and a mobile computing device comprising a device processor configured with processor-executable instructions to perform operations comprising; sending behavior vectors to the server; receiving the mobile device classifier from the server; and classifying a behavior of the mobile computing device based on the received mobile device classifier.
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22. A server, comprising:
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means for applying machine learning techniques to generate a first family of classifier models using a boosted decision tree to describe a corpus of behavior vectors; means for computing an answer ratio for one or more nodes of the boosted decision tree; and means for determining which factors in the first family of classifier models have a high probability of enabling a mobile device to conclusively determine whether a mobile device behavior is not benign based on computed answer ratios. - View Dependent Claims (23, 24)
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25. A server, comprising:
a processor configured with processor-executable instructions to perform operations comprising; applying machine learning techniques to generate a first family of classifier models using a boosted decision tree to describe a corpus of behavior vectors; computing an answer ratio for one or more nodes of the boosted decision tree; and determining which factors in the first family of classifier models have a high probability of enabling a mobile device to conclusively determine whether a mobile device behavior is not benign based on computed answer ratios. - View Dependent Claims (26, 27)
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28. A non-transitory computer readable storage medium having stored thereon server-executable software instructions configured to cause a server processor to perform operations for generating data models in a communication system, comprising:
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applying machine learning techniques to generate a first family of classifier models using a boosted decision tree to describe a corpus of behavior vectors; computing an answer ratio for one or more nodes of the boosted decision tree; and determining which factors in the first family of classifier models have a high probability of enabling a mobile device to conclusively determine whether a mobile device behavior is not benign based on computed answer ratios. - View Dependent Claims (29, 30)
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Specification