Malware Analysis of Imaged Binary Samples by Convolutional Neural Network with Attention Mechanism

Abstract

This paper presents a method to extract important byte sequences in malware samples by application of convolutional neural network (CNN) to images converted from binary data. This method, by combining a technique called the attention mechanism into CNN, enables calculation of an "attention map," which shows regions having higher importance for classification in the image. The extracted region with higher importance can provide useful information for human analysts who investigate the functionalities of unknown malware samples. Results of our evaluation experiment using malware dataset show that the proposed method provides higher classification accuracy than a conventional method. Furthermore, analysis of malware samples based on the calculated attention map confirmed that the extracted sequences provide useful information for manual analysis.