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Research on the Influencing Factors of LLVM IR Optimization Effect

2023 IEEE 3rd International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA)(2023)

College of Electronic Engineering

Cited 0|Views7
Abstract
The intermediate representation has a natural advantage in solving the problem of cross-architecture binary code similarity detection, which can greatly reduce the amount of data required for machine learning model training and improve the versatility and scalability of the model. optimizing the intermediate representation can solve the problem of binary differences caused by different compilation architectures, compilers, optimization options, and obfuscation strategies, which is conducive to improving the accuracy of binary code similarity detection. We explored the effects of compilation architectures, compilers, optimization options and obfuscation strategies on the optimization of intermediate representations through four experiments. The experimental results show that the compilers GCC and Clang at the time of binary file compilation have a significant improvement on the optimization effect of intermediate representation. In addition, the optimization effect using opt-Ol and opt-O2 is the best, and the similarity can be improved by up to 21.7%. The compiler architectures XS6_32, XS6_64, ARM32, ARM64 and optimization options −OO, −Ol, −O2, −O3 have little effect on the optimization effect of the intermediate representation. The compiler architecture MIP32 and obfuscation strategies F1a, Sub, Bcf, All have a negative effect on the optimization effect of intermediate representation.
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LLVM IR,binary file,optimization effect,control variable method
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要点】:研究了LLVM IR优化效果的影响因素,发现编译架构、编译器、优化选项和混淆策略对中间表示的优化效果有显著影响,实验结果显示了不同因素对优化效果的具体影响。

方法】:通过四个实验探索了编译架构、编译器、优化选项和混淆策略对中间表示优化的影响。

实验】:实验结果显示,GCC和Clang编译器在二进制文件编译时对中间表示优化效果有显著改善,使用opt-Ol和opt-O2优化效果最佳,相似性能提高了21.7%。而MIP32编译器架构和F1a、Sub、Bcf、All混淆策略对中间表示的优化效果有负面影响。