@inproceedings{feng-etal-2020-neureduce,
title = "{N}eu{R}educe: Reducing Mixed {B}oolean-Arithmetic Expressions by Recurrent Neural Network",
author = "Feng, Weijie and
Liu, Binbin and
Xu, Dongpeng and
Zheng, Qilong and
Xu, Yun",
editor = "Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.56/",
doi = "10.18653/v1/2020.findings-emnlp.56",
pages = "635--644",
abstract = "Mixed Boolean-Arithmetic (MBA) expressions involve both arithmetic calculation (e.g.,plus, minus, multiply) and bitwise computation (e.g., and, or, negate, xor). MBA expressions have been widely applied in software obfuscation, transforming programs from a simple form to a complex form. MBA expressions are challenging to be simplified, because the interleaving bitwise and arithmetic operations causing mathematical reduction laws to be ineffective. Our goal is to recover the original, simple form from an obfuscated MBA expression. In this paper, we first propose NeuReduce, a string to string method based on neural networks to automatically learn and reduce complex MBA expressions. We develop a comprehensive MBA dataset, including one million diversified MBA expression samples and corresponding simplified forms. After training on the dataset, NeuReduce can reduce MBA rules to homelier but mathematically equivalent forms. By comparing with three state-of-the-art MBA reduction methods, our evaluation result shows that NeuReduce outperforms all other tools in terms of accuracy, solving time, and performance overhead."
}
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<abstract>Mixed Boolean-Arithmetic (MBA) expressions involve both arithmetic calculation (e.g.,plus, minus, multiply) and bitwise computation (e.g., and, or, negate, xor). MBA expressions have been widely applied in software obfuscation, transforming programs from a simple form to a complex form. MBA expressions are challenging to be simplified, because the interleaving bitwise and arithmetic operations causing mathematical reduction laws to be ineffective. Our goal is to recover the original, simple form from an obfuscated MBA expression. In this paper, we first propose NeuReduce, a string to string method based on neural networks to automatically learn and reduce complex MBA expressions. We develop a comprehensive MBA dataset, including one million diversified MBA expression samples and corresponding simplified forms. After training on the dataset, NeuReduce can reduce MBA rules to homelier but mathematically equivalent forms. By comparing with three state-of-the-art MBA reduction methods, our evaluation result shows that NeuReduce outperforms all other tools in terms of accuracy, solving time, and performance overhead.</abstract>
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%0 Conference Proceedings
%T NeuReduce: Reducing Mixed Boolean-Arithmetic Expressions by Recurrent Neural Network
%A Feng, Weijie
%A Liu, Binbin
%A Xu, Dongpeng
%A Zheng, Qilong
%A Xu, Yun
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F feng-etal-2020-neureduce
%X Mixed Boolean-Arithmetic (MBA) expressions involve both arithmetic calculation (e.g.,plus, minus, multiply) and bitwise computation (e.g., and, or, negate, xor). MBA expressions have been widely applied in software obfuscation, transforming programs from a simple form to a complex form. MBA expressions are challenging to be simplified, because the interleaving bitwise and arithmetic operations causing mathematical reduction laws to be ineffective. Our goal is to recover the original, simple form from an obfuscated MBA expression. In this paper, we first propose NeuReduce, a string to string method based on neural networks to automatically learn and reduce complex MBA expressions. We develop a comprehensive MBA dataset, including one million diversified MBA expression samples and corresponding simplified forms. After training on the dataset, NeuReduce can reduce MBA rules to homelier but mathematically equivalent forms. By comparing with three state-of-the-art MBA reduction methods, our evaluation result shows that NeuReduce outperforms all other tools in terms of accuracy, solving time, and performance overhead.
%R 10.18653/v1/2020.findings-emnlp.56
%U https://aclanthology.org/2020.findings-emnlp.56/
%U https://doi.org/10.18653/v1/2020.findings-emnlp.56
%P 635-644
Markdown (Informal)
[NeuReduce: Reducing Mixed Boolean-Arithmetic Expressions by Recurrent Neural Network](https://aclanthology.org/2020.findings-emnlp.56/) (Feng et al., Findings 2020)
ACL