<monospace>o-glasses</monospace>: Visualizing X86 Code From Binary Using a 1D-CNN

Abstract

Malicious document files used in targeted attacks often contain a small program called shellcode. It is often hard to prepare a runnable environment for dynamic analysis of these document files because they exploit specific vulnerabilities. In these cases, it is necessary to identify the position of the shellcode in each document file to analyze it. If the exploit code uses executable scripts such as JavaScript and Flash, it is not so hard to locate the shellcode. On the other hand, it is sometimes almost impossible to locate the shellcode when it does not contain any JavaScript or Flash but consists of native x86 code only. Binary fragment classification is often applied to visualize the location of regions of interest, and shellcode must contain at least a small fragment of x86 native code even if most of it is obfuscated, such as a decoder for the obfuscated body of the shellcode. In this paper, we propose a novel method, o-glasses, to visualize the shellcode by recognizing the x86 native code using a specially designed one-dimensional convolutional neural network (1d-CNN). The fragment size needs to be as small as the minimum size of the x86 native code in the whole shellcode. Our results show that a 16-instruction-sequence (approximately 48 bytes on average) is sufficient for the code fragment visualization. Our method, o-glasses (1d-CNN), outperforms other methods in that it recognizes x86 native code with a surprisingly high F-measure rate (about 99.95%).

Keywords

• Binary analysis
• CNN
• machine learning
• MLP
• shellcode
• visualization