{"id":"https://openalex.org/W2749008552","doi":"https://doi.org/10.1145/3133956.3134018","title":"Neural Network-based Graph Embedding for Cross-Platform Binary Code Similarity Detection","display_name":"Neural Network-based Graph Embedding for Cross-Platform Binary Code Similarity Detection","publication_year":2017,"publication_date":"2017-10-27","ids":{"openalex":"https://openalex.org/W2749008552","doi":"https://doi.org/10.1145/3133956.3134018","mag":"2749008552"},"language":"en","primary_location":{"id":"doi:10.1145/3133956.3134018","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3133956.3134018","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1708.06525","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Xiaojun Xu","orcid":null},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xiaojun Xu","raw_affiliation_strings":["Shanghai Jiao Tong University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Chang Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I95457486","display_name":"University of California, Berkeley","ror":"https://ror.org/01an7q238","country_code":"US","type":"education","lineage":["https://openalex.org/I95457486"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chang Liu","raw_affiliation_strings":["University of California, Berkeley, Berkeley, CA, USA"],"affiliations":[{"raw_affiliation_string":"University of California, Berkeley, Berkeley, CA, USA","institution_ids":["https://openalex.org/I95457486"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Qian Feng","orcid":null},"institutions":[{"id":"https://openalex.org/I4210101778","display_name":"Samsung (United States)","ror":"https://ror.org/01bfbvm65","country_code":"US","type":"company","lineage":["https://openalex.org/I2250650973","https://openalex.org/I4210101778"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Qian Feng","raw_affiliation_strings":["Samsung Research America, Mountain View, CA, USA"],"affiliations":[{"raw_affiliation_string":"Samsung Research America, Mountain View, CA, USA","institution_ids":["https://openalex.org/I4210101778"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Heng Yin","orcid":null},"institutions":[{"id":"https://openalex.org/I103635307","display_name":"University of California, Riverside","ror":"https://ror.org/03nawhv43","country_code":"US","type":"education","lineage":["https://openalex.org/I103635307"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Heng Yin","raw_affiliation_strings":["University of California, Riverside, Riverside, CA, USA"],"affiliations":[{"raw_affiliation_string":"University of California, Riverside, Riverside, CA, USA","institution_ids":["https://openalex.org/I103635307"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Le Song","orcid":null},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Le Song","raw_affiliation_strings":["Georgia Institute of Technology, Atlanta, GA, USA"],"affiliations":[{"raw_affiliation_string":"Georgia Institute of Technology, Atlanta, GA, USA","institution_ids":["https://openalex.org/I130701444"]}]},{"author_position":"last","author":{"id":null,"display_name":"Dawn Song","orcid":null},"institutions":[{"id":"https://openalex.org/I95457486","display_name":"University of California, Berkeley","ror":"https://ror.org/01an7q238","country_code":"US","type":"education","lineage":["https://openalex.org/I95457486"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dawn Song","raw_affiliation_strings":["University of California, Berkeley, Berkeley, CA, USA"],"affiliations":[{"raw_affiliation_string":"University of California, Berkeley, Berkeley, CA, USA","institution_ids":["https://openalex.org/I95457486"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I183067930"],"apc_list":null,"apc_paid":null,"fwci":18.1295,"has_fulltext":false,"cited_by_count":493,"citation_normalized_percentile":{"value":0.99583077,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"363","last_page":"376"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11241","display_name":"Advanced Malware Detection Techniques","score":0.9994000196456909,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11241","display_name":"Advanced Malware Detection Techniques","score":0.9994000196456909,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10260","display_name":"Software Engineering Research","score":0.9889000058174133,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10743","display_name":"Software Testing and Debugging Techniques","score":0.9850000143051147,"subfield":{"id":"https://openalex.org/subfields/1712","display_name":"Software"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.5986999869346619},{"id":"https://openalex.org/keywords/binary-number","display_name":"Binary number","score":0.5},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.47110000252723694},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.47110000252723694},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.4595000147819519},{"id":"https://openalex.org/keywords/binary-code","display_name":"Binary code","score":0.43050000071525574},{"id":"https://openalex.org/keywords/firmware","display_name":"Firmware","score":0.4235999882221222},{"id":"https://openalex.org/keywords/graph-embedding","display_name":"Graph embedding","score":0.4196999967098236}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7340999841690063},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5986999869346619},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5335999727249146},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.5},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.47110000252723694},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.47110000252723694},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.4595000147819519},{"id":"https://openalex.org/C63435697","wikidata":"https://www.wikidata.org/wiki/Q864135","display_name":"Binary code","level":3,"score":0.43050000071525574},{"id":"https://openalex.org/C67212190","wikidata":"https://www.wikidata.org/wiki/Q104851","display_name":"Firmware","level":2,"score":0.4235999882221222},{"id":"https://openalex.org/C75564084","wikidata":"https://www.wikidata.org/wiki/Q5597085","display_name":"Graph embedding","level":3,"score":0.4196999967098236},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4163999855518341},{"id":"https://openalex.org/C541664917","wikidata":"https://www.wikidata.org/wiki/Q14001","display_name":"Malware","level":2,"score":0.3950999975204468},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3919000029563904},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.38119998574256897},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.38029998540878296},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.3716000020503998},{"id":"https://openalex.org/C27458966","wikidata":"https://www.wikidata.org/wiki/Q1187693","display_name":"Control flow graph","level":2,"score":0.3662000000476837},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.36550000309944153},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.36480000615119934},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.3521000146865845},{"id":"https://openalex.org/C160191386","wikidata":"https://www.wikidata.org/wiki/Q868299","display_name":"Control flow","level":2,"score":0.30070000886917114},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.28130000829696655},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.27149999141693115}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3133956.3134018","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3133956.3134018","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1708.06525","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1708.06525","pdf_url":"https://arxiv.org/pdf/1708.06525","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1708.06525","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1708.06525","pdf_url":"https://arxiv.org/pdf/1708.06525","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1506372999","https://openalex.org/W1546956568","https://openalex.org/W1690253345","https://openalex.org/W1875842236","https://openalex.org/W1888005072","https://openalex.org/W2012604743","https://openalex.org/W2048693719","https://openalex.org/W2051990174","https://openalex.org/W2091939272","https://openalex.org/W2112725702","https://openalex.org/W2116341502","https://openalex.org/W2127589108","https://openalex.org/W2142498761","https://openalex.org/W2145056020","https://openalex.org/W2154851992","https://openalex.org/W2156718197","https://openalex.org/W2170607286","https://openalex.org/W2577142429","https://openalex.org/W2919115771","https://openalex.org/W2962756421","https://openalex.org/W3003413895","https://openalex.org/W6619192471","https://openalex.org/W6727823310"],"related_works":[],"abstract_inverted_index":{"The":[0],"problem":[1],"of":[2,86,148,194],"cross-platform":[3],"binary":[4,13,88],"code":[5],"similarity":[6,92,130],"detection":[7,93,131],"aims":[8],"at":[9],"detecting":[10],"whether":[11],"two":[12,106],"functions":[14],"coming":[15],"from":[16,156],"different":[17],"platforms":[18],"are":[19,44],"similar":[20],"or":[21],"not.":[22],"It":[23],"has":[24],"many":[25],"security":[26,199],"applications,":[27],"including":[28],"plagiarism":[29],"detection,":[30,32],"malware":[31],"vulnerability":[33],"search,":[34],"etc.":[35],"Existing":[36],"approaches":[37,123],"rely":[38],"on":[39,81,197],"approximate":[40],"graph-matching":[41],"algorithms,":[42],"which":[43],"inevitably":[45],"slow":[46],"and":[47,50,150],"sometimes":[48],"inaccurate,":[49],"hard":[51],"to":[52,54,72,129,145,162,165],"adapt":[53],"a":[55,67,77,110,191],"new":[56],"task.":[57],"To":[58],"address":[59],"these":[60],"issues,":[61],"in":[62],"this":[63],"work,":[64],"we":[65],"propose":[66],"novel":[68],"neural":[69],"network-based":[70],"approach":[71],"compute":[73],"the":[74,82,91,100,103,121,152,184],"embedding,":[75],"i.e.,":[76,186],"numeric":[78],"vector,":[79],"based":[80],"control":[83],"flow":[84],"graph":[85],"each":[87],"function,":[89],"then":[90],"can":[94,135,176],"be":[95],"done":[96],"efficiently":[97],"by":[98,124,143],"measuring":[99],"distance":[101],"between":[102],"embeddings":[104],"for":[105],"functions.":[107],"We":[108],"implement":[109],"prototype":[111],"called":[112],"Gemini.":[113],"Our":[114,168,188],"extensive":[115],"evaluation":[116],"shows":[117],"that":[118,174],"Gemini":[119,134,175],"outperforms":[120],"state-of-the-art":[122],"large":[125],"margins":[126],"with":[127],"respect":[128],"accuracy.":[132],"Further,":[133],"speed":[136],"up":[137],"prior":[138],"art's":[139],"embedding":[140],"generation":[141],"time":[142,155],"3":[144],"4":[146],"orders":[147],"magnitude":[149],"reduce":[151],"required":[153],"training":[154],"more":[157,179],"than":[158,183],"1":[159],"week":[160],"down":[161],"30":[163],"minutes":[164],"10":[166],"hours.":[167],"real":[169],"world":[170],"case":[171],"studies":[172],"demonstrate":[173],"identify":[177],"significantly":[178],"vulnerable":[180],"firmware":[181],"images":[182],"state-of-the-art,":[185],"Genius.":[187],"research":[189],"showcases":[190],"successful":[192],"application":[193],"deep":[195],"learning":[196],"computer":[198],"problems.":[200]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":85},{"year":2024,"cited_by_count":63},{"year":2023,"cited_by_count":84},{"year":2022,"cited_by_count":74},{"year":2021,"cited_by_count":87},{"year":2020,"cited_by_count":49},{"year":2019,"cited_by_count":36},{"year":2018,"cited_by_count":12}],"updated_date":"2026-03-31T07:56:22.981413","created_date":"2017-08-31T00:00:00"}