METHODS AND SYSTEMS OF USING BOOSTED DECISION STUMPS AND JOINT FEATURE SELECTION AND CULLING ALGORITHMS FOR THE EFFICIENT CLASSIFICATION OF MOBILE DEVICE BEHAVIORS

摘要

Methods and systems for classifying mobile device behavior include configuring a server use a large corpus of mobile device behaviors to generate a full classifier model that includes a finite state machine suitable for conversion into boosted decision stumps and/or which describes all or many of the features relevant to determining whether a mobile device behavior is benign or contributing to the mobile device's degradation over time. A mobile device may receive the full classifier model and use the model to generate a full set of boosted decision stumps from which a more focused or lean classifier model is generated by culling the full set to a subset suitable for efficiently determining whether mobile device behavior are benign. Boosted decision stumps may be culled by selecting all boosted decision stumps that depend upon a limited set of test conditions.