Malicious Code Detection based on Image Processing Using Deep Learning

In this study, we have used the Image Similarity technique to detect the unknown or new type of malware using CNN approach. CNN was investigated and tested with three types of datasets i.e. one from Vision Research Lab, which contains 9458 gray-scale images that have been extracted from the same number of malware samples that come from 25 different malware families, and second was benign dataset which contained 3000 different kinds of benign software. Benign dataset and dataset vision research lab were initially executable files which were converted in to binary code and then converted in to image files. We obtained a testing accuracy of 98% on Vision Research dataset.