Biblio

The similarity detection of the program has important significance in code reuse, plagiarism detection, intellectual property protection and information retrieval methods. Attribute counting methods cannot take into account program semantics. The method based on syntax tree or graph structure has a very high construction cost and low space efficiency. So it is difficult to solve problems in large-scale software systems. This paper uses different decision strategies for different levels, then puts forward a similarity detection method at the file level. This method can make full use of the features of the program and take into account the space-time efficiency. By using static analysis methods, we get function features and control flow features of files. And based on this, we establish the function call graph. The similar degree between two files can be measured with the two graphs. Experimental results show the method can effectively detect similar files. Finally, this paper discusses the direction of development of this method.

The problem of cross-platform binary code similarity detection aims at detecting whether two binary functions coming from different platforms are similar or not. It has many security applications, including plagiarism detection, malware detection, vulnerability search, etc. Existing approaches rely on approximate graph-matching algorithms, which are inevitably slow and sometimes inaccurate, and hard to adapt to a new task. To address these issues, in this work, we propose a novel neural network-based approach to compute the embedding, i.e., a numeric vector, based on the control flow graph of each binary function, then the similarity detection can be done efficiently by measuring the distance between the embeddings for two functions. We implement a prototype called Gemini. Our extensive evaluation shows that Gemini outperforms the state-of-the-art approaches by large margins with respect to similarity detection accuracy. Further, Gemini can speed up prior art's embedding generation time by 3 to 4 orders of magnitude and reduce the required training time from more than 1 week down to 30 minutes to 10 hours. Our real world case studies demonstrate that Gemini can identify significantly more vulnerable firmware images than the state-of-the-art, i.e., Genius. Our research showcases a successful application of deep learning on computer security problems.