X-MBA: Towards Heterogeneous Mixed Boolean-Arithmetic Deobfuscation

The Mixed Boolean-Arithmetic (MBA) expression has been widely used by attackers as an effective obfuscation schema to hide malicious code and conceal sensitive data. For deobfuscating MBAs, the approaches based on signature vector offer excellent performance but struggle with MBA expressions involving nonzero constants or bit manipulators like Extract or Shift. We refer to such expressions as heterogeneous MBAs. In this paper, we focus on heterogeneous MBA deobfuscation. We found that any linear heterogeneous MBA expression can be partitioned as a disjoint set of homogeneous fragments, each of which can be simplified separately. Based on this observation, we proposed X-MBA, the neXt generation MBA deobfuscator. Our methodology has undergone several experiments, demonstrating that X-MBA outperforms SOTAs on different types of heterogeneous MBAs with only negligible compromises.

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Publication Details

Published
2024-10-28
DOI
https://doi.org/10.1109/milcom61039.2024.10773656
Primary Topic
Algorithms and Data Compression
Type
article
Field-Weighted Citation Impact
0.00

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article

X-MBA: Towards Heterogeneous Mixed Boolean-Arithmetic Deobfuscation

Gengwang Li, Dongliang Fang, Min Yu, Gang Li et al.
Algorithms and Data Compression
article

X-MBA: Towards Heterogeneous Mixed Boolean-Arithmetic Deobfuscation

Gengwang Li, Dongliang Fang, Min Yu, Gang Li, Xiang Meng, Weiqing Huang, Jiangguo Jiang
article en

Abstract

The Mixed Boolean-Arithmetic (MBA) expression has been widely used by attackers as an effective obfuscation schema to hide malicious code and conceal sensitive data. For deobfuscating MBAs, the approaches based on signature vector offer excellent performance but struggle with MBA expressions involving nonzero constants or bit manipulators like Extract or Shift. We refer to such expressions as heterogeneous MBAs. In this paper, we focus on heterogeneous MBA deobfuscation. We found that any linear heterogeneous MBA expression can be partitioned as a disjoint set of homogeneous fragments, each of which can be simplified separately. Based on this observation, we proposed X-MBA, the neXt generation MBA deobfuscator. Our methodology has undergone several experiments, demonstrating that X-MBA outperforms SOTAs on different types of heterogeneous MBAs with only negligible compromises.

Deakin University (AU), Chinese Academy of Sciences (CN), Institute of Information Engineering (CN)
Youth Innovation Promotion Association
Openalex Percentile: Top 80%
Algorithms and Data Compression
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