| Title | Understand Code Style: Efficient CNN-Based Compiler Optimization Recognition System |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Yang, Shouguo, Shi, Zhiqiang, Zhang, Guodong, Li, Mingxuan, Ma, Yuan, Sun, Limin |
| Conference Name | ICC 2019 - 2019 IEEE International Conference on Communications (ICC) |
| Date Published | may |
| Keywords | Analytical models, binary file processing, binary security analysis, closed source compilers, CNN-based compiler optimization level recognition model, CNN-based compiler optimization recognition system, compiler optimization algorithms, compiler security, compositionality, convolution, convolutional neural nets, Metrics, optimising compilers, Optimization, pubcrawl, recurrent neural nets, Resiliency, RNN-based model, Scalability, security, security of data, Semantics, Software, Task Analysis, understand code style |
| Abstract | Compiler optimization level recognition can be applied to vulnerability discovery and binary analysis. Due to the exists of many different compilation optimization options, the difference in the contents of the binary file is very complicated. There are thousands of compiler optimization algorithms and multiple different processor architectures, so it is very difficult to manually analyze binary files and recognize its compiler optimization level with rules. This paper first proposes a CNN-based compiler optimization level recognition model: BinEye. The system extracts semantic and structural differences and automatically recognize the compiler optimization levels. The model is designed to be very suitable for binary file processing and is easy to understand. We built a dataset containing 80028 binary files for the model training and testing. Our proposed model achieves an accuracy of over 97%. At the same time, BinEye is a fully CNN-based system and it has a faster forward calculation speed, at least 8 times faster than the normal RNN-based model. Through our analysis of the model output, we successfully found the difference in assembly codes caused by the different compiler optimization level. This means that the model we proposed is interpretable. Based on our model, we propose a method to analyze the code differences caused by different compiler optimization levels, which has great guiding significance for analyzing closed source compilers and binary security analysis. |
| DOI | 10.1109/ICC.2019.8761073 |
| Citation Key | yang_understand_2019 |
Understand Code Style: Efficient CNN-Based Compiler Optimization Recognition System