Binary code similarity analysis based on naming function and common vector space

• Published in: Scientific reports 2023-09, Vol.13 (1), p.15676-15676, Article 15676
• Main Authors: Xia, Bing, Pang, Jianmin, Zhou, Xin, Shan, Zheng, Wang, Junchao, Yue, Feng
• Format: Article
• Language: English
• Subjects: 639/705/117; 639/705/794; Codes; Deep learning; Embedding; Flow control; Humanities and Social Sciences; multidisciplinary; Science; Science (multidisciplinary); Semantics
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-023-42769-9

Binary code similarity analysis is widely used in the field of vulnerability search where source code may not be available to detect whether two binary functions are similar or not. Based on deep learning and natural processing techniques, several approaches have been proposed to perform cross-platform binary code similarity analysis using control flow graphs. However, existing schemes suffer from the shortcomings of large differences in instruction syntaxes across different target platforms, inability to align control flow graph nodes, and less introduction of high-level semantics of stability, which pose challenges for identifying similar computations between binary functions of different platforms generated from the same source code. We argue that extracting stable, platform-independent semantics can improve model accuracy, and a cross-platform binary function similarity comparison model N_Match is proposed. The model elevates different platform instructions to the same semantic space to shield their underlying platform instruction differences, uses graph embedding technology to learn the stability semantics of neighbors, extracts high-level knowledge of naming function to alleviate the differences brought about by cross-platform and cross-optimization levels, and combines the stable graph structure as well as the stable, platform-independent API knowledge of naming function to represent the final semantics of functions. The experimental results show that the model accuracy of N_Match outperforms the baseline model in terms of cross-platform, cross-optimization level, and industrial scenarios. In the vulnerability search experiment, N_Match significantly improves hit@N, the mAP exceeds the current graph embedding model by 66%. In addition, we also give several interesting observations from the experiments. The code and model are publicly available at https://www.github.com/CSecurityZhongYuan/Binary-Name_Match .