一般混合布尔算术表达式的简化:GAMBA

Benjamin Reichenwallner, Peter Meerwald-Stadler
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引用次数: 0

摘要

恶意软件代码通常采用各种自我保护技术来使分析复杂化。其中一种技术是应用混合布尔算术(Mixed-Boolean Arithmetic, MBA)表达式来创建不透明的谓词,并使数据流多样化和模糊化。在这项工作中,我们旨在为在非常实际的背景下简化非线性MBA表达式提供工具,以在一代辛勤的、多样化的MBA及其分析之间的军备竞赛中竞争。本文提出的算法GAMBA以代数重写为核心,对SiMBA进行了扩展[19]。它从最广泛测试的公共数据集中实现了MBA表达式的有效解混淆,并在大多数情况下将表达式简化为其基本事实,超越了同行工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Simplification of General Mixed Boolean-Arithmetic Expressions: GAMBA
Malware code often resorts to various self-protection techniques to complicate analysis. One such technique is applying Mixed-Boolean Arithmetic (MBA) expressions as a way to create opaque predicates and diversify and obfuscate the data flow. In this work we aim to provide tools for the simplification of nonlinear MBA expressions in a very practical context to compete in the arms race between the generation of hard, diverse MBAs and their analysis. The proposed algorithm GAMBA employs algebraic rewriting at its core and extends SiMBA [19]. It achieves efficient deobfuscation of MBA expressions from the most widely tested public datasets and simplifies expressions to their ground truths in most cases, surpassing peer tools.
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