Machine learning classification using Markov modeling
AssigneeCylance Inc.
InventorsJian Luan, Derek Soeder
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Abstract
Systems, methods, and articles of manufacture, including computer program products, are provided for classification systems and methods using modeling. In some example embodiments, there is provided a system that includes at least one processor and at least one memory including program code which when executed by the at least one memory provides operations. The operations can include generating a representation of a sequence of sections of a file and/or determining, from a model including conditional probabilities, a probability for each transition between at least two sequential sections in the representation. The operations can further include classifying the file based on the probabilities for each transition.
Claims38
A method for implementation by one or more processors forming part of at least one computing device, the method comprising:
- generating a representation of a sequence of sections of a file;
- determining, from a model including conditional probabilities, a probability for each transition between at least two sequential sections in the representation, the model being trained using a recursive neural network;
- and
- classifying the file based on the probabilities for each transition;
- wherein:
- the representation comprises a string of alphanumeric characters in which each character in the string corresponds to a different section of the file within the sequence;
- the sequence of the sections of the file are arranged according to a virtual ordering, the virtual ordering representing an order in which the sections of the file will occur in memory, the generated sequence of the sections of the file being different than an order in which at least a portion of the sections occur within the file when not executing.
A method for implementation by one or more processors forming part of at least one computing device, the method comprising:
- generating a representation of a sequence of sections of a file;
- determining, from a model including conditional probabilities, a probability for each transition between at least two sequential sections in the representation, the model being trained using a land change modeler (LCM) recursive neural network;
- and
- classifying the file based on the probabilities for each transition;
- wherein:
- the representation comprise a string of alphanumeric characters in which each character in the string corresponds to a different section of the file within the sequence.
Dependencies are inferred from the claim text.