Classification of Malware programs using autoencoders based deep learning architecture and its application to the microsoft malware Classification challenge (BIG 2015) dataset

Abstract

Distinguishing and classifying different types of malware is important to better understanding how they can infect computers and devices, the threat level they pose and how to protect against them. In this paper, a system for classifying malware programs is presented. The paper describes the architecture of the system and assesses its performance on a publicly available database (provided by Microsoft for the Microsoft Malware Classification Challenge BIG2015) to serve as a benchmark for future research efforts. First, the malicious programs are preprocessed such that they are visualized as gray scale images. We then make use of an architecture comprised of multiple layers (multiple levels of encoding) to carry out the classification process of those images/programs. We compare the performance of this approach against traditional machine learning and pattern recognition algorithms. Our experimental results show that the deep learning architecture yields a boost in performance over those conventional/standard algorithms. A hold-out validation analysis using the superior architecture shows an accuracy in the order of 99.15%.