SoK: Automatic Deobfuscation of Virtualization-protected Applications

Patrick Kochberger, S. Schrittwieser, Stefan Schweighofer, Peter Kieseberg, E. Weippl
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引用次数: 2

Abstract

Malware authors often rely on code obfuscation to hide the malicious functionality of their software, making detection and analysis more difficult. One of the most advanced techniques for binary obfuscation is virtualization-based obfuscation, which converts the functionality of a program into the bytecode of a randomly generated virtual machine which is embedded into the protected program. To enable the automatic detection and analysis of protected malware, new deobfuscation techniques against virtualization-based obfuscation are constantly being developed and proposed in the literature. In this work, we systematize existing knowledge of automatic deobfuscation of virtualization-protected programs in a novel classification scheme and evaluate where we stand in the arms race between malware authors and code analysts in regards to virtualization-based obfuscation. In addition to a theoretical discussion of different types of deobfuscation methodologies, we present an in-depth practical evaluation that compares state-of-the-art virtualization-based obfuscators with currently available deobfuscation tools. The results clearly indicate the possibility of automatic deobfuscation of virtualization-based obfuscation in specific scenarios. Furthermore, however, the results highlight limitations of existing deobfuscation methods. Multiple challenges still lie ahead on the way towards reliable and resilient automatic deobfuscation of virtualization-based obfuscation.
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SoK:虚拟化保护应用程序的自动解混淆
恶意软件的作者通常依靠代码混淆来隐藏其软件的恶意功能,使检测和分析更加困难。最先进的二进制混淆技术之一是基于虚拟化的混淆,它将程序的功能转换为随机生成的虚拟机的字节码,该虚拟机嵌入到受保护的程序中。为了能够自动检测和分析受保护的恶意软件,针对基于虚拟化的混淆的新去混淆技术正在不断地被开发和提出。在这项工作中,我们在一种新的分类方案中系统化了现有的虚拟化保护程序的自动去混淆知识,并评估了我们在恶意软件作者和代码分析师之间关于基于虚拟化的混淆的军备竞赛中的地位。除了对不同类型的去混淆方法进行理论讨论之外,我们还提出了一个深入的实践评估,将最先进的基于虚拟化的混淆器与当前可用的去混淆工具进行比较。结果清楚地表明,在特定场景中,可以自动消除基于虚拟化的混淆。此外,然而,结果突出了现有的去混淆方法的局限性。在实现基于虚拟化的混淆的可靠和灵活的自动去混淆的道路上,仍然面临着许多挑战。
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