Extracting Scheme of Compiler Information using Convolutional Neural Networks in Stripped Binaries

Jungsoo Lee, Hyunwoong Choi, Junyeong Heo
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引用次数: 0

Abstract

The strip binary is a binary from which debug symbol information has been deleted, and therefore it is difficult to analyze the binary through techniques such as reverse engineering. Traditional binary analysis tools rely on debug symbolic information to analyze binaries, making it difficult to detect or analyze malicious code with features of these strip binaries. In order to solve this problem, the need for a technology capable of effectively extracting the information of the strip binary has emerged. In paper, focusing the fact that the byte code of the binary file is generated very differently depending on compiler version, optimazer level, etc. For effective compiler version extraction, the entire byte code is read and imaged as the target of the stripped binaries and this is applied to the convolution neural network. Finally, we achieve an accuracy of 93.5%, and we provide an opportunity to analyze stripped binary more effectively than before.
基于卷积神经网络的编译器信息提取方案
条带二进制文件是一种删除了调试符号信息的二进制文件,因此很难通过逆向工程等技术分析该二进制文件。传统的二进制分析工具依赖于调试符号信息来分析二进制文件,很难检测或分析具有这些条带二进制文件特征的恶意代码。为了解决这一问题,需要一种能够有效提取条带二进制信息的技术。在本文中,重点关注二进制文件的字节码的生成非常不同,这取决于编译器版本,优化器级别等。为了有效地提取编译器版本,读取整个字节码并将其成像为剥离二进制文件的目标,并将其应用于卷积神经网络。最后,我们实现了93.5%的精度,并且我们提供了一个比以前更有效地分析剥离二进制的机会。
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