{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/neural-network-based-graph-embedding-for","title":"Neural Network-based Graph Embedding for Cross-Platform Binary Code Similarity Detection","arxiv_id":"1708.06525","date":"2017-08-22","proceeding":null,"authors":["Xiaojun Xu","Chang Liu","Qian Feng","Heng Yin","Le Song","Dawn Song"],"abstract":"The problem of cross-platform binary code similarity detection aims at\ndetecting whether two binary functions coming from different platforms are\nsimilar or not. It has many security applications, including plagiarism\ndetection, malware detection, vulnerability search, etc. Existing approaches\nrely on approximate graph matching algorithms, which are inevitably slow and\nsometimes inaccurate, and hard to adapt to a new task. To address these issues,\nin this work, we propose a novel neural network-based approach to compute the\nembedding, i.e., a numeric vector, based on the control flow graph of each\nbinary function, then the similarity detection can be done efficiently by\nmeasuring the distance between the embeddings for two functions. We implement a\nprototype called Gemini. Our extensive evaluation shows that Gemini outperforms\nthe state-of-the-art approaches by large margins with respect to similarity\ndetection accuracy. Further, Gemini can speed up prior art's embedding\ngeneration time by 3 to 4 orders of magnitude and reduce the required training\ntime from more than 1 week down to 30 minutes to 10 hours. Our real world case\nstudies demonstrate that Gemini can identify significantly more vulnerable\nfirmware images than the state-of-the-art, i.e., Genius. Our research showcases\na successful application of deep learning on computer security problems.","url_abs":"http://arxiv.org/abs/1708.06525v4","url_pdf":"http://arxiv.org/pdf/1708.06525v4.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"neural-network-based-graph-embedding-for","repo_url":"https://github.com/xiaojunxu/dnn-binary-code-similarity","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"computer-security","task_name":"Computer Security"},{"task_slug":"graph-embedding","task_name":"Graph Embedding"},{"task_slug":"graph-matching","task_name":"Graph Matching"},{"task_slug":"malware-detection","task_name":"Malware Detection"}],"methods":[{"method_slug":"speed","method_name":"SPEED"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1708.06525","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}