{"id":"https://openalex.org/W7128717104","doi":"https://doi.org/10.1109/jiot.2026.3664076","title":"CDAF: A Contrastive Distribution Alignment Framework for Generative Augmentation in Imbalanced IIoT Intrusion Detection","display_name":"CDAF: A Contrastive Distribution Alignment Framework for Generative Augmentation in Imbalanced IIoT Intrusion Detection","publication_year":2026,"publication_date":"2026-02-12","ids":{"openalex":"https://openalex.org/W7128717104","doi":"https://doi.org/10.1109/jiot.2026.3664076"},"language":null,"primary_location":{"id":"doi:10.1109/jiot.2026.3664076","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jiot.2026.3664076","pdf_url":null,"source":{"id":"https://openalex.org/S2480266640","display_name":"IEEE Internet of Things Journal","issn_l":"2327-4662","issn":["2327-4662","2372-2541"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Internet of Things Journal","raw_type":"journal-article"},"type":"article","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/A5101528374","display_name":"Guangzhao Chai","orcid":"https://orcid.org/0009-0003-7824-9471"},"institutions":[{"id":"https://openalex.org/I184843921","display_name":"Hebei University of Technology","ror":"https://ror.org/018hded08","country_code":"CN","type":"education","lineage":["https://openalex.org/I184843921"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Guangzhao Chai","raw_affiliation_strings":["School of Artificial Intelligence, Hebei University of Technology, Tianjin, China"],"raw_orcid":"https://orcid.org/0009-0003-7824-9471","affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Hebei University of Technology, Tianjin, China","institution_ids":["https://openalex.org/I184843921"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034766193","display_name":"Youxi Wu","orcid":"https://orcid.org/0000-0001-5314-3468"},"institutions":[{"id":"https://openalex.org/I184843921","display_name":"Hebei University of Technology","ror":"https://ror.org/018hded08","country_code":"CN","type":"education","lineage":["https://openalex.org/I184843921"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Youxi Wu","raw_affiliation_strings":["School of Artificial Intelligence, Hebei University of Technology, Tianjin, China"],"raw_orcid":"https://orcid.org/0000-0001-5314-3468","affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Hebei University of Technology, Tianjin, China","institution_ids":["https://openalex.org/I184843921"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5125733344","display_name":"Jing Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I184843921","display_name":"Hebei University of Technology","ror":"https://ror.org/018hded08","country_code":"CN","type":"education","lineage":["https://openalex.org/I184843921"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jing Liu","raw_affiliation_strings":["School of Artificial Intelligence, Hebei University of Technology, Tianjin, China"],"raw_orcid":"https://orcid.org/0000-0002-0232-1444","affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Hebei University of Technology, Tianjin, China","institution_ids":["https://openalex.org/I184843921"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5101528374"],"corresponding_institution_ids":["https://openalex.org/I184843921"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.24232258,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"13","issue":"8","first_page":"17774","last_page":"17789"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10400","display_name":"Network Security and Intrusion Detection","score":0.34850001335144043,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T10400","display_name":"Network Security and Intrusion Detection","score":0.34850001335144043,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.20980000495910645,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.12770000100135803,"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/margin","display_name":"Margin (machine learning)","score":0.6074000000953674},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5843999981880188},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.5013999938964844},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.48579999804496765},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.47519999742507935},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4731999933719635},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.46810001134872437},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.46369999647140503}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8210999965667725},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.660099983215332},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.6074000000953674},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5843999981880188},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.5013999938964844},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.48579999804496765},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.47519999742507935},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4731999933719635},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.46810001134872437},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.46369999647140503},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43479999899864197},{"id":"https://openalex.org/C35525427","wikidata":"https://www.wikidata.org/wiki/Q745881","display_name":"Intrusion detection system","level":2,"score":0.42149999737739563},{"id":"https://openalex.org/C2780992000","wikidata":"https://www.wikidata.org/wiki/Q17016113","display_name":"Generator (circuit theory)","level":3,"score":0.39100000262260437},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.3675000071525574},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3264999985694885},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.3206000030040741},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.320499986410141},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3188999891281128},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.3149000108242035},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.2703999876976013}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/jiot.2026.3664076","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jiot.2026.3664076","pdf_url":null,"source":{"id":"https://openalex.org/S2480266640","display_name":"IEEE Internet of Things Journal","issn_l":"2327-4662","issn":["2327-4662","2372-2541"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Internet of Things Journal","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6774216890335083,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[{"id":"https://openalex.org/G6611307210","display_name":null,"funder_award_id":"2024YFB3311901","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"},{"id":"https://openalex.org/G8082894880","display_name":null,"funder_award_id":"62372154","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"In":[0],"industrial":[1],"Internet":[2],"of":[3,19,142],"Things":[4],"(IIoT)":[5],"intrusion":[6],"detection,":[7],"severe":[8],"class":[9],"imbalance":[10],"and":[11,46,69,78,97,129,162,177],"inter-class":[12,122,159],"feature":[13,85,156],"overlap":[14,157],"often":[15],"degrade":[16],"the":[17,59,63,91,125,135,143,163],"performance":[18],"traditional":[20],"classifiers":[21],"in":[22,134],"recognizing":[23],"minority":[24],"classes.":[25,107],"This":[26],"paper":[27],"proposes":[28],"a":[29,47,53,83,111],"Contrastive":[30],"Distribution":[31],"Alignment":[32],"Framework":[33],"(CDAF),":[34],"which":[35,120],"aims":[36],"to":[37,65],"achieve":[38],"high-quality":[39],"generative":[40,144],"augmentation":[41],"through":[42],"feature-space":[43],"alignment":[44,86],"constraints":[45],"curriculum-based":[48],"training":[49,127],"strategy.":[50],"CDAF":[51],"learns":[52],"structured":[54],"latent":[55],"space":[56],"conditioned":[57],"on":[58,131,170],"target":[60],"class,":[61],"enabling":[62,139],"generator":[64],"produce":[66],"more":[67],"representative":[68],"class-consistent":[70],"synthetic":[71],"samples.":[72],"The":[73],"framework":[74,165],"further":[75],"constructs":[76],"\u201cgenerated\u2013target\u201d":[77],"\u201cgenerated\u2013non-target\u201d":[79],"sample":[80],"pairings,":[81],"introducing":[82],"contrastive":[84,151],"regularization":[87],"term":[88],"that":[89,149],"minimizes":[90],"distributional":[92],"distance":[93,160],"between":[94],"generated":[95],"samples":[96],"target-class":[98],"features,":[99],"while":[100],"simultaneously":[101],"enlarging":[102],"their":[103],"margin":[104],"from":[105],"non-target":[106],"Furthermore,":[108],"we":[109,147],"design":[110],"stage-wise":[112],"weight":[113],"transfer":[114],"mechanism":[115],"termed":[116],"ECLG":[117],"(Enhanced-Contrast":[118],"Late-Generative),":[119],"emphasizes":[121],"discrimination":[123],"during":[124],"early":[126],"phase":[128],"focuses":[130],"generation":[132],"quality":[133],"later":[136],"phase,":[137],"thereby":[138],"dynamic":[140],"optimization":[141],"distribution.":[145],"Empirically,":[146],"show":[148],"CDAF\u2019s":[150],"enhancement":[152],"loss":[153],"effectively":[154],"reduces":[155],"under":[158],"constraints,":[161],"overall":[164],"significantly":[166],"improves":[167],"minority-class":[168],"detection":[169],"three":[171],"benchmark":[172],"IIoT":[173],"datasets":[174],"(X-IIoTID,":[175],"WUSTL-IIoT-2021,":[176],"ToN":[178],"IoT).":[179]},"counts_by_year":[],"updated_date":"2026-05-05T08:41:31.759640","created_date":"2026-02-13T00:00:00"}