{"id":"https://openalex.org/W4416062129","doi":"https://doi.org/10.1145/3779208.3785264","title":"Taming Data Challenges in ML-based Security Tasks Using Generative AI","display_name":"Taming Data Challenges in ML-based Security Tasks Using Generative AI","publication_year":2026,"publication_date":"2026-06-01","ids":{"openalex":"https://openalex.org/W4416062129","doi":"https://doi.org/10.1145/3779208.3785264"},"language":"en","primary_location":{"id":"doi:10.1145/3779208.3785264","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3779208.3785264","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM Asia Conference on Computer and Communications Security","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3779208.3785264","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5076866329","display_name":"Shravya Kanchi","orcid":"https://orcid.org/0009-0005-4088-8408"},"institutions":[{"id":"https://openalex.org/I859038795","display_name":"Virginia Tech","ror":"https://ror.org/02smfhw86","country_code":"US","type":"education","lineage":["https://openalex.org/I859038795"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shravya Kanchi","raw_affiliation_strings":["Virginia Tech, Blacksburg, USA"],"raw_orcid":"https://orcid.org/0009-0005-4088-8408","affiliations":[{"raw_affiliation_string":"Virginia Tech, Blacksburg, USA","institution_ids":["https://openalex.org/I859038795"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051506534","display_name":"Neal Mangaokar","orcid":"https://orcid.org/0000-0002-0684-4971"},"institutions":[{"id":"https://openalex.org/I27837315","display_name":"University of Michigan","ror":"https://ror.org/00jmfr291","country_code":"US","type":"education","lineage":["https://openalex.org/I27837315"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Neal Mangaokar","raw_affiliation_strings":["University of Michigan, Ann Arbor, USA"],"raw_orcid":"https://orcid.org/0000-0002-0684-4971","affiliations":[{"raw_affiliation_string":"University of Michigan, Ann Arbor, USA","institution_ids":["https://openalex.org/I27837315"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041581395","display_name":"Aravind Cheruvu","orcid":"https://orcid.org/0009-0003-7247-0490"},"institutions":[{"id":"https://openalex.org/I859038795","display_name":"Virginia Tech","ror":"https://ror.org/02smfhw86","country_code":"US","type":"education","lineage":["https://openalex.org/I859038795"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Aravind Cheruvu","raw_affiliation_strings":["Virginia Tech, Blacksburg, USA"],"raw_orcid":"https://orcid.org/0009-0003-7247-0490","affiliations":[{"raw_affiliation_string":"Virginia Tech, Blacksburg, USA","institution_ids":["https://openalex.org/I859038795"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080038295","display_name":"Sifat Muhammad Abdullah","orcid":"https://orcid.org/0009-0008-2285-7490"},"institutions":[{"id":"https://openalex.org/I859038795","display_name":"Virginia Tech","ror":"https://ror.org/02smfhw86","country_code":"US","type":"education","lineage":["https://openalex.org/I859038795"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sifat Muhammad Abdullah","raw_affiliation_strings":["Virginia Tech, Blacksburg, USA"],"raw_orcid":"https://orcid.org/0009-0008-2285-7490","affiliations":[{"raw_affiliation_string":"Virginia Tech, Blacksburg, USA","institution_ids":["https://openalex.org/I859038795"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076332929","display_name":"Shirin Nilizadeh","orcid":"https://orcid.org/0000-0002-0539-3742"},"institutions":[{"id":"https://openalex.org/I189196454","display_name":"The University of Texas at Arlington","ror":"https://ror.org/019kgqr73","country_code":"US","type":"education","lineage":["https://openalex.org/I189196454"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shirin Nilizadeh","raw_affiliation_strings":["University of Texas, Arlington, USA"],"raw_orcid":"https://orcid.org/0000-0002-0539-3742","affiliations":[{"raw_affiliation_string":"University of Texas, Arlington, USA","institution_ids":["https://openalex.org/I189196454"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019723791","display_name":"Atul Prakash","orcid":"https://orcid.org/0000-0002-4907-3687"},"institutions":[{"id":"https://openalex.org/I27837315","display_name":"University of Michigan","ror":"https://ror.org/00jmfr291","country_code":"US","type":"education","lineage":["https://openalex.org/I27837315"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Atul Prakash","raw_affiliation_strings":["University of Michigan, Ann Arbor, USA"],"raw_orcid":"https://orcid.org/0000-0002-4907-3687","affiliations":[{"raw_affiliation_string":"University of Michigan, Ann Arbor, USA","institution_ids":["https://openalex.org/I27837315"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5022718146","display_name":"Bimal Viswanath","orcid":"https://orcid.org/0000-0002-6559-9689"},"institutions":[{"id":"https://openalex.org/I859038795","display_name":"Virginia Tech","ror":"https://ror.org/02smfhw86","country_code":"US","type":"education","lineage":["https://openalex.org/I859038795"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Bimal Viswanath","raw_affiliation_strings":["Virginia Tech, Blacksburg, USA"],"raw_orcid":"https://orcid.org/0000-0002-6559-9689","affiliations":[{"raw_affiliation_string":"Virginia Tech, Blacksburg, USA","institution_ids":["https://openalex.org/I859038795"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.02107551,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"788","last_page":"804"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12761","display_name":"Data Stream Mining Techniques","score":0.6061000227928162,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T12761","display_name":"Data Stream Mining Techniques","score":0.6061000227928162,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T11241","display_name":"Advanced Malware Detection Techniques","score":0.15399999916553497,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.06109999865293503,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/classifier","display_name":"Classifier (UML)","score":0.6191999912261963},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4814999997615814},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.46950000524520874},{"id":"https://openalex.org/keywords/labeled-data","display_name":"Labeled data","score":0.3968000113964081},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.3407000005245209},{"id":"https://openalex.org/keywords/data-security","display_name":"Data security","score":0.31439998745918274},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.2930000126361847}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7937999963760376},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6191999912261963},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6082000136375427},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5321999788284302},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4814999997615814},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.46950000524520874},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.3968000113964081},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.3407000005245209},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3328000009059906},{"id":"https://openalex.org/C10511746","wikidata":"https://www.wikidata.org/wiki/Q899388","display_name":"Data security","level":3,"score":0.31439998745918274},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.2930000126361847},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.29089999198913574},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.27390000224113464},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.25839999318122864},{"id":"https://openalex.org/C60777511","wikidata":"https://www.wikidata.org/wiki/Q3045002","display_name":"Concept drift","level":3,"score":0.2551000118255615},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.25369998812675476},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.2533000111579895},{"id":"https://openalex.org/C2781170535","wikidata":"https://www.wikidata.org/wiki/Q30587856","display_name":"Noisy data","level":2,"score":0.2526000142097473},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.2524000108242035},{"id":"https://openalex.org/C121687571","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Activity recognition","level":2,"score":0.25200000405311584}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1145/3779208.3785264","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3779208.3785264","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM Asia Conference on Computer and Communications Security","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2507.06092","is_oa":true,"landing_page_url":"https://arxiv.org/abs/2507.06092","pdf_url":"https://arxiv.org/pdf/2507.06092","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:doi:10.48550/arxiv.2507.06092","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2507.06092","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2507.06092","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.1145/3779208.3785264","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3779208.3785264","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM Asia Conference on Computer and Communications Security","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1270524059","display_name":"Collaborative Research: SaTC: CORE: Medium: Practical Generative AI to Enhance Security Classifiers by Overcoming Data Challenges","funder_award_id":"2453819","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G4698887634","display_name":"Collaborative Research: SaTC: CORE: Medium: Practical Generative AI to Enhance Security Classifiers by Overcoming Data Challenges","funder_award_id":"2453820","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7654700372","display_name":"Collaborative Research: SaTC: CORE: Medium: Practical Generative AI to Enhance Security Classifiers by Overcoming Data Challenges","funder_award_id":"2453818","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":4,"referenced_works":["https://openalex.org/W2794601036","https://openalex.org/W3088157974","https://openalex.org/W2963197901","https://openalex.org/W2789828921"],"related_works":[],"abstract_inverted_index":{"Machine":[0],"learning-based":[1],"supervised":[2],"classifiers":[3,29],"are":[4],"widely":[5],"used":[6],"for":[7,203],"security":[8,76,108,163,204],"tasks,":[9,171],"and":[10,49,83,158,179],"their":[11],"improvement":[12],"has":[13],"been":[14],"largely":[15],"focused":[16],"on":[17,161],"algorithmic":[18],"advancements.":[19],"Data":[20],"challenges":[21,48],"that":[22,91,99,128,150,191],"negatively":[23],"impact":[24],"the":[25,36,105,142,196],"performance":[26,106,185],"of":[27,107,112,169,198],"these":[28],"have":[30],"received":[31],"limited":[32],"attention.":[33],"We":[34,53,69,97,165,189],"address":[35,46],"following":[37],"research":[38],"question:":[39],"Can":[40],"developments":[41],"in":[42,117,141],"Generative":[43],"AI":[44],"(GenAI)":[45],"data":[47,60,95],"improve":[50,66,104],"classifier":[51,67],"performance?":[52],"propose":[54],"augmenting":[55],"training":[56,123],"datasets":[57],"with":[58],"synthetic":[59],"generated":[61],"using":[62,78,187],"GenAI":[63,81,87,100,129,152,200],"techniques":[64,101],"to":[65,114,134,155],"generalization.":[68],"evaluate":[70],"this":[71],"approach":[72],"across":[73],"7":[74],"diverse":[75],"tasks":[77],"6":[79],"state-of-the-art":[80],"methods":[82],"introduce":[84],"a":[85],"novel":[86],"scheme":[88],"called":[89],"Nimai":[90],"enables":[92],"highly":[93],"controlled":[94],"synthesis.":[96],"find":[98],"can":[102,130],"significantly":[103],"classifiers,":[109],"achieving":[110],"improvements":[111],"up":[113],"32.6%":[115],"even":[116],"severely":[118],"data-constrained":[119],"settings":[120],"(only":[121],"~180":[122],"samples).":[124],"Furthermore,":[125],"we":[126],"demonstrate":[127],"facilitate":[131],"rapid":[132],"adaptation":[133],"concept":[135],"drift":[136],"post-deployment,":[137],"requiring":[138],"minimal":[139],"labeling":[140],"adjustment":[143],"process.":[144],"Despite":[145],"successes,":[146],"our":[147,192],"study":[148,193],"finds":[149],"some":[151],"schemes":[153],"struggle":[154],"initialize":[156],"(train":[157],"produce":[159],"data)":[160],"certain":[162],"tasks.":[164,205],"also":[166],"identify":[167],"characteristics":[168],"specific":[170],"such":[172],"as":[173],"noisy":[174],"labels,":[175],"overlapping":[176],"class":[177],"distributions,":[178],"sparse":[180],"feature":[181],"vectors,":[182],"which":[183],"hinder":[184],"boost":[186],"GenAI.":[188],"believe":[190],"will":[194],"drive":[195],"development":[197],"future":[199],"tools":[201],"designed":[202]},"counts_by_year":[],"updated_date":"2026-08-11T07:18:39.950985","created_date":"2025-10-10T00:00:00"}