System and method for analyzing binary code for malware classification using artificial neural network techniques

A system, implemented with non-transitory storage medium including software that, when executed, identifies whether a file is associated with a cyber-attack, the system comprising:

• a pre-processor being configured to (i) select a section of content from the file received as input and (ii) generate a first representation of the content;
• a neural network communicatively coupled to the pre-processor, the neural network being configured to process a network input being the first representation of the content by at least applying a plurality of weighting operations executing a programmatic function on the first representation to produce a network output, the programmatic function being trained using a training set including labeled patterns associated with at least one of (i) benign files and (ii) files associated with a cyber-attack;
• a classifier communicatively coupled to the neural network, the classifier being configured to (i) receive the network output from the neural network and (ii) determine a classification assigned to the file based, at least in part, on a threat score corresponding to a scalar value generated from received network output from the neural network;
• and
• a message generator configured to generate a message in response to determining the classification of the file as being associated with a cyber-attack.