{"id":"https://openalex.org/W4406612144","doi":"https://doi.org/10.1109/smc54092.2024.10831120","title":"Combining Deep Learning and Expert Rules for Smart Contract Vulnerability Detection","display_name":"Combining Deep Learning and Expert Rules for Smart Contract Vulnerability Detection","publication_year":2024,"publication_date":"2024-10-06","ids":{"openalex":"https://openalex.org/W4406612144","doi":"https://doi.org/10.1109/smc54092.2024.10831120"},"language":"en","primary_location":{"id":"doi:10.1109/smc54092.2024.10831120","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc54092.2024.10831120","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5049099445","display_name":"Senlin Ren","orcid":"https://orcid.org/0009-0001-9177-070X"},"institutions":[{"id":"https://openalex.org/I78675632","display_name":"Beijing Information Science & Technology University","ror":"https://ror.org/04xnqep60","country_code":"CN","type":"education","lineage":["https://openalex.org/I78675632"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Senlin Ren","raw_affiliation_strings":["School of Computer Science, Beijing Information Science and Technology University,Beijing,China,100101"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science, Beijing Information Science and Technology University,Beijing,China,100101","institution_ids":["https://openalex.org/I78675632"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100588569","display_name":"Jun Yang","orcid":null},"institutions":[{"id":"https://openalex.org/I78675632","display_name":"Beijing Information Science & Technology University","ror":"https://ror.org/04xnqep60","country_code":"CN","type":"education","lineage":["https://openalex.org/I78675632"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jun Yang","raw_affiliation_strings":["School of Computer Science, Beijing Information Science and Technology University,Beijing,China,100101"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science, Beijing Information Science and Technology University,Beijing,China,100101","institution_ids":["https://openalex.org/I78675632"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046975787","display_name":"Xiguo Gu","orcid":"https://orcid.org/0009-0004-2618-946X"},"institutions":[{"id":"https://openalex.org/I78675632","display_name":"Beijing Information Science & Technology University","ror":"https://ror.org/04xnqep60","country_code":"CN","type":"education","lineage":["https://openalex.org/I78675632"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiguo Gu","raw_affiliation_strings":["School of Computer Science, Beijing Information Science and Technology University,Beijing,China,100101"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science, Beijing Information Science and Technology University,Beijing,China,100101","institution_ids":["https://openalex.org/I78675632"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100655623","display_name":"Liwei Zheng","orcid":"https://orcid.org/0000-0001-7641-6369"},"institutions":[{"id":"https://openalex.org/I78675632","display_name":"Beijing Information Science & Technology University","ror":"https://ror.org/04xnqep60","country_code":"CN","type":"education","lineage":["https://openalex.org/I78675632"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liwei Zheng","raw_affiliation_strings":["School of Computer Science, Beijing Information Science and Technology University,Beijing,China,100101"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science, Beijing Information Science and Technology University,Beijing,China,100101","institution_ids":["https://openalex.org/I78675632"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5071655887","display_name":"Zhanqi Cui","orcid":"https://orcid.org/0000-0002-5537-9236"},"institutions":[{"id":"https://openalex.org/I78675632","display_name":"Beijing Information Science & Technology University","ror":"https://ror.org/04xnqep60","country_code":"CN","type":"education","lineage":["https://openalex.org/I78675632"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhanqi Cui","raw_affiliation_strings":["School of Computer Science, Beijing Information Science and Technology University,Beijing,China,100101"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science, Beijing Information Science and Technology University,Beijing,China,100101","institution_ids":["https://openalex.org/I78675632"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I78675632"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1598","last_page":"1603"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12394","display_name":"Insurance and Financial Risk Management","score":0.9391999840736389,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T12394","display_name":"Insurance and Financial Risk Management","score":0.9391999840736389,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T13643","display_name":"Artificial Intelligence in Law","score":0.9369000196456909,"subfield":{"id":"https://openalex.org/subfields/3320","display_name":"Political Science and International Relations"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T12519","display_name":"Cybercrime and Law Enforcement Studies","score":0.9085000157356262,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6529408693313599},{"id":"https://openalex.org/keywords/vulnerability","display_name":"Vulnerability (computing)","score":0.6328598260879517},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.49997568130493164},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4931597113609314},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3903135657310486},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.361236035823822}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6529408693313599},{"id":"https://openalex.org/C95713431","wikidata":"https://www.wikidata.org/wiki/Q631425","display_name":"Vulnerability (computing)","level":2,"score":0.6328598260879517},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.49997568130493164},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4931597113609314},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3903135657310486},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.361236035823822}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/smc54092.2024.10831120","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc54092.2024.10831120","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W2539190473","https://openalex.org/W2778144710","https://openalex.org/W2805052744","https://openalex.org/W2805827286","https://openalex.org/W2970809537","https://openalex.org/W3003036212","https://openalex.org/W3035733952","https://openalex.org/W3121385022","https://openalex.org/W4281384435","https://openalex.org/W4308642997","https://openalex.org/W4313569595","https://openalex.org/W4319587111","https://openalex.org/W4376606614","https://openalex.org/W4384009709","https://openalex.org/W4384345641","https://openalex.org/W4385194706","https://openalex.org/W6757822707"],"related_works":["https://openalex.org/W2731899572","https://openalex.org/W2961085424","https://openalex.org/W3215138031","https://openalex.org/W4306674287","https://openalex.org/W3009238340","https://openalex.org/W4360585206","https://openalex.org/W4321369474","https://openalex.org/W4285208911","https://openalex.org/W4387369504","https://openalex.org/W3046775127"],"abstract_inverted_index":{"Smart":[0,105],"contracts":[1,17,31],"usually":[2,76],"hold":[3],"a":[4,93],"large":[5],"amount":[6],"of":[7,38,113,152,173,177,191,199,209,224],"digital":[8],"assets,":[9],"which":[10,219],"can":[11],"cause":[12],"substantial":[13],"losses":[14],"if":[15],"these":[16],"have":[18,47],"vulnerabilities.":[19,85],"Thus,":[20],"it":[21],"is":[22],"essential":[23],"to":[24,78,83,109,140,148],"adequately":[25],"detect":[26,84],"possible":[27],"vulnerabilities":[28,39,53,64,210],"in":[29,40,197],"smart":[30,41,119,143,226],"before":[32,133],"deployment.":[33],"There":[34],"are":[35,65],"many":[36],"types":[37],"contracts,":[42],"and":[43,101,126,135,195,204,215],"different":[44],"detection":[45,74,145,171,179,232],"methods":[46],"their":[48],"own":[49],"unique":[50],"advantages,":[51],"some":[52,63],"may":[54],"be":[55],"more":[56,66],"suitable":[57,67,160],"for":[58,68,104,162,206],"expert":[59,127,137],"rule-based":[60],"methods,":[61],"while":[62],"deep":[69,123],"learning-based":[70],"methods.":[71,180],"A":[72],"single":[73,178],"method":[75,117,146,161,185],"fails":[77],"fully":[79],"use":[80],"its":[81],"ability":[82],"To":[86],"address":[87],"the":[88,111,142,150,153,159,169,183,200,225],"above":[89],"problems,":[90],"we":[91],"propose":[92],"composite":[94],"approach":[95],"named":[96],"CDE-VD":[97,174,187],"(Combining":[98],"Deep":[99],"Learning":[100],"Expert":[102],"Rules":[103],"Contract":[106],"Vulnerability":[107],"Detection)":[108],"improve":[110,230],"performance":[112,172],"vulnerability":[114,170,231],"detection.":[115,163],"The":[116,164],"divides":[118],"contract":[120,144,227],"samples":[121,129,154,228],"into":[122],"learning-prone":[124],"sam-ples":[125],"rule-prone":[128],"by":[130],"classifying":[131],"them":[132],"detection,":[134],"extracts":[136],"rule":[138],"features":[139],"train":[141],"classifier":[147],"predict":[149],"category":[151,222],"under":[155],"analysis,":[156],"then":[157],"selects":[158],"experimental":[165],"results":[166],"show":[167],"that":[168,176,221],"outperforms":[175],"Compared":[181],"with":[182],"SOTA":[184],"MANDO,":[186],"achieves":[188],"average":[189],"improvements":[190],"3.22%,":[192],"2.32%,":[193],"9.25%,":[194],"6.54%":[196],"terms":[198],"Accuracy,":[201],"Precision,":[202],"Recall,":[203],"F1-score":[205],"five":[207],"categories":[208],"such":[211],"as":[212],"access":[213],"control":[214],"time":[216],"manipulation,":[217],"respectively,":[218],"indicates":[220],"prediction":[223],"could":[229],"performance.":[233]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
