Building multi-representational learning models for static analysis of source code

A method, comprising:

• receiving training data, wherein the training data includes a set of source code files for training a multi-representational learning (MRL) model for classifying malicious source code and benign source code based on a static analysis;
• generating a first feature vector based on a set of characters extracted from the set of source code files;
• generating a second feature vector based on a set of tokens extracted from the set of source code files;
• and
• performing an ensemble of the first feature vector and the second feature vector to form a target feature vector for classifying malicious source code and benign source code based on the static analysis.

A computer program product embodied in a tangible computer readable storage medium and comprising computer instructions for:

• receiving training data, wherein the training data includes a set of source code files for training a multi-representational learning (MRL) model for classifying malicious source code and benign source code based on a static analysis;
• generating a first feature vector based on a set of characters extracted from the set of source code files;
• generating a second feature vector based on a set of tokens extracted from the set of source code files;
• and
• performing an ensemble of the first feature vector and the second feature vector to form a target feature vector for classifying malicious source code and benign source code based on the static analysis.