Malware classification using gray-scale images and ensemble learning
Liu Liu, Baosheng Wang · 2016
Malware authors can easily generate obfuscated and metamorphic malware to evade the detection of anti-virus software using automated toolkits. To identify these variations, we design an efficient system to detect and classify the malicious software. We construct a controlled disassembly files from malware. And we convert disassembly files into gray-scale images. In order to improve the efficiency, we use the local mean method to compress gray-scale images, which are mapped into feature vectors. To classify malware, we propose a novel ensemble learning which is based on K-means and the diversity selection. Finally, our experiments show that our method is able to effectively classify the malware.