Binary function similarity detection based on text semantics

Abstract

Binary code similarity is that different binary codes obtained from the same source code compiled by different compiler configurations are similar. Binary code similarity detection is often used to evaluate whether functions in two binary codes are similar. This technique has critical applications in intellectual property protection and IoT security, such as code plagiarism detection, malware detection, vulnerability detection, etc. In this paper, we propose a text semantics-based binary function similarity detection model SBFS, which firstly transforms binary functions into function texts by preprocessing assembly instructions; then learns function texts to obtain semantic embedding vectors using a natural language processing model; Finally, the similarity between two functions is measured by calculating the cosine distance between the embedding vectors of the two functions. Experimental results show that the SBFS model can achieve crossarchitecture detection and higher accuracy with 98.2% in the binary function similarity detection task.