Self-training classification
AssigneeForescout Technologies, Inc.
InventorsSiying Yang, Yang Zhang
Life of this patent
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Abstract
Systems, methods, and related technologies for self-training classification are described. In certain aspects, a plurality of device classification methods with associated models are accessed. Each of the classification methods have an associated reliability level. The models of classification methods with a higher reliability level than other classifications methods are used to train the models associated with lower reliability level. The trained models and associated classification methods are thus improved.
Claims21
A training method comprising:
- accessing a plurality of device classification methods, wherein each of the plurality of methods has a respective associated model, and wherein each of the plurality of methods has a respective associated reliability level;
- generating a respective data set associated with each of the device classification methods based on classifying a plurality of devices communicatively coupled to a network;
- selecting a first device classification method and a second device classification method of the plurality of device classification methods, wherein the first device classification method has a higher reliability level than the second device classification method;
- determining a training data set using a respective data set associated with the first device classification method;
- training, by a processing device, the second device classification method model using the training data set;
- and
- storing the trained second device classification model.
A system comprising:
- a memory;
- and
- a processing device, operatively coupled to the memory, to:
- access a plurality of device classification methods, wherein each of the plurality of methods has a respective associated model, and wherein each of the plurality of methods has a respective associated reliability level;
- generate a respective data set associated with each of the device classification methods based on classifying a plurality of devices communicatively coupled to a network;
- select a first device classification method and a second device classification method of the plurality of device classification methods, wherein the first device classification method has a higher reliability level than the second device classification method;
- determine a training data set using a respective data set associated with the first device classification method;
- train the second device classification method model using the training data set;
- and
- store the trained second device classification model.
A non-transitory computer readable medium having instructions encoded thereon that, when executed by a processing device, cause the processing device to:
- access a plurality of device classification methods, wherein each of the plurality of methods has a respective associated model, and wherein each of the plurality of methods has a respective associated reliability level;
- generate a respective data set associated with each of the device classification methods based on classifying a plurality of devices communicatively coupled to a network;
- select a first device classification method and a second device classification method of the plurality of device classification methods, wherein the first device classification method has a higher reliability level than the second device classification method;
- determine a training data set using a respective data set associated with the first device classification method;
- train, by the processing device, the second device classification method model using the training data set;
- and
- storing the trained second device classification model.
Dependencies are inferred from the claim text.