Fake Malware Classification with CNN via Image Conversion

Abstract

Improvements in malware detection techniques have grown significantly over the past decade. These improvements have resulted in better security for systems from various forms of malware attacks. However, it is also the reason for continuous evolution of malware which makes it harder for current security mechanisms to detect them. Hence, there is a need to understand different malwares and study classification techniques using the ever-evolving field of machine learning. The goal of this research project is to identify similarities between malware families and to improve on classification of malwares within different malware families by implementing Convolutional Neural Networks (CNNs) on their executable files. Moreover, there are different algorithms through which we can resize images. Classifying these malware images will help us understand effectiveness of the techniques. As malwares evolve continuously, we will generate fake malware image samples using Auxiliary Classifier Generative Adversarial Network (AC-GANs) and jumble the original dataset to try and break the CNN classifier.