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Recent works leverage deep neural networks (DNN) to learn a numeric vector representation (namely,\n            <jats:italic>embeddings<\/jats:italic>\n            ) of assembly functions, enabling similarity analysis in the numeric space. However, existing DNN-based techniques capture syntactic-, control flow-, or data flow-level information of assembly code, which is too coarse-grained to represent program functionality. These methods can suffer from low robustness to challenging settings such as compiler optimizations and obfuscations.\n          <\/jats:p>\n          <jats:p>\n            We present\n            <jats:monospace>sem2vec<\/jats:monospace>\n            , a binary code embedding framework that learns from\n            <jats:italic>semantics<\/jats:italic>\n            . Given the control-flow graph (CFG), 34 pages. of an assembly function, we divide it into\n            <jats:italic>tracelets<\/jats:italic>\n            , denoting continuous and short execution traces that are reachable from the function entry point. We use symbolic execution to extract symbolic constraints and other auxiliary information on each tracelet. We then train masked language models to compute embeddings of symbolic execution outputs. Last, we use graph neural networks, to aggregate tracelet embeddings into the CFG-level embedding for a function. Our evaluation shows that\n            <jats:monospace>sem2vec<\/jats:monospace>\n            extracts high-quality embedding and is robust against different compilers, optimizations, architectures, and popular obfuscation methods including virtualization obfuscation. We further augment a vulnerability search application with embeddings computed by\n            <jats:monospace>sem2vec<\/jats:monospace>\n            and demonstrate a significant improvement in vulnerability search accuracy.\n          <\/jats:p>","DOI":"10.1145\/3569933","type":"journal-article","created":{"date-parts":[[2022,10,28]],"date-time":"2022-10-28T11:47:34Z","timestamp":1666957654000},"page":"1-34","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":23,"title":["<tt>sem2vec<\/tt>\n            : Semantics-aware Assembly Tracelet Embedding"],"prefix":"10.1145","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1066-0331","authenticated-orcid":false,"given":"Huaijin","family":"Wang","sequence":"first","affiliation":[{"name":"Hong Kong University of Science and Technology, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7680-2817","authenticated-orcid":false,"given":"Pingchuan","family":"Ma","sequence":"additional","affiliation":[{"name":"Hong Kong University of Science and Technology, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0866-0308","authenticated-orcid":false,"given":"Shuai","family":"Wang","sequence":"additional","affiliation":[{"name":"Hong Kong University of Science and Technology, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8200-7518","authenticated-orcid":false,"given":"Qiyi","family":"Tang","sequence":"additional","affiliation":[{"name":"Keen Security Lab, Tencent, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4154-2941","authenticated-orcid":false,"given":"Sen","family":"Nie","sequence":"additional","affiliation":[{"name":"Keen Security Lab, Tencent, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6842-7487","authenticated-orcid":false,"given":"Shi","family":"Wu","sequence":"additional","affiliation":[{"name":"Keen Security Lab, Tencent, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,5,27]]},"reference":[{"key":"e_1_3_2_2_2","unstructured":"2014. 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