Image visualization based malware detection

Abstract

Malware detection is one of the challenging tasks in Cyber security. The advent of code obfuscation, metamorphic malware, packers and zero day attacks has made malware detection a challenging task. In this paper we present a visualization based approach for malware detection. First the executable is converted to a gray-scale image called byteplot. Later we extract low level features like intensity based and texture based features. We apply computationally intelligent techniques for malware detection using these features. In this work we used Support Vector Machines (SVMs) and obtained an accuracy of 95% on a dataset containing 25000 malware and 12000 benign samples.