{"id":"https://openalex.org/W4405304552","doi":"https://doi.org/10.1109/tifs.2024.3516547","title":"Fed-UGI: Federated Undersampling Learning Framework With Gini Impurity for Imbalanced Network Intrusion Detection","display_name":"Fed-UGI: Federated Undersampling Learning Framework With Gini Impurity for Imbalanced Network Intrusion Detection","publication_year":2024,"publication_date":"2024-12-12","ids":{"openalex":"https://openalex.org/W4405304552","doi":"https://doi.org/10.1109/tifs.2024.3516547"},"language":"en","primary_location":{"id":"doi:10.1109/tifs.2024.3516547","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tifs.2024.3516547","pdf_url":null,"source":{"id":"https://openalex.org/S61310614","display_name":"IEEE Transactions on Information Forensics and Security","issn_l":"1556-6013","issn":["1556-6013","1556-6021"],"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 Transactions on Information Forensics and Security","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/A5016980869","display_name":"Ming Zheng","orcid":"https://orcid.org/0000-0001-9001-0859"},"institutions":[{"id":"https://openalex.org/I4472751","display_name":"Anhui Normal University","ror":"https://ror.org/05fsfvw79","country_code":"CN","type":"education","lineage":["https://openalex.org/I4472751"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Ming Zheng","raw_affiliation_strings":["School of Computer and Information, Anhui Normal University, Wuhu, China"],"raw_orcid":"https://orcid.org/0000-0001-9001-0859","affiliations":[{"raw_affiliation_string":"School of Computer and Information, Anhui Normal University, Wuhu, China","institution_ids":["https://openalex.org/I4472751"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Xiaowen Hu","orcid":"https://orcid.org/0009-0002-6872-434X"},"institutions":[{"id":"https://openalex.org/I4472751","display_name":"Anhui Normal University","ror":"https://ror.org/05fsfvw79","country_code":"CN","type":"education","lineage":["https://openalex.org/I4472751"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaowen Hu","raw_affiliation_strings":["School of Computer and Information, Anhui Normal University, Wuhu, China"],"raw_orcid":"https://orcid.org/0009-0002-6872-434X","affiliations":[{"raw_affiliation_string":"School of Computer and Information, Anhui Normal University, Wuhu, China","institution_ids":["https://openalex.org/I4472751"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110401351","display_name":"Ying Hu","orcid":"https://orcid.org/0000-0001-9952-6288"},"institutions":[{"id":"https://openalex.org/I4472751","display_name":"Anhui Normal University","ror":"https://ror.org/05fsfvw79","country_code":"CN","type":"education","lineage":["https://openalex.org/I4472751"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ying Hu","raw_affiliation_strings":["School of Computer and Information, Anhui Normal University, Wuhu, China"],"raw_orcid":"https://orcid.org/0000-0001-9952-6288","affiliations":[{"raw_affiliation_string":"School of Computer and Information, Anhui Normal University, Wuhu, China","institution_ids":["https://openalex.org/I4472751"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070055083","display_name":"Xiaoyao Zheng","orcid":"https://orcid.org/0000-0001-7554-4211"},"institutions":[{"id":"https://openalex.org/I4472751","display_name":"Anhui Normal University","ror":"https://ror.org/05fsfvw79","country_code":"CN","type":"education","lineage":["https://openalex.org/I4472751"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoyao Zheng","raw_affiliation_strings":["School of Computer and Information, Anhui Normal University, Wuhu, China"],"raw_orcid":"https://orcid.org/0000-0001-7554-4211","affiliations":[{"raw_affiliation_string":"School of Computer and Information, Anhui Normal University, Wuhu, China","institution_ids":["https://openalex.org/I4472751"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5050720898","display_name":"Yonglong Luo","orcid":"https://orcid.org/0000-0003-4987-0376"},"institutions":[{"id":"https://openalex.org/I4472751","display_name":"Anhui Normal University","ror":"https://ror.org/05fsfvw79","country_code":"CN","type":"education","lineage":["https://openalex.org/I4472751"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yonglong Luo","raw_affiliation_strings":["School of Computer and Information, Anhui Normal University, Wuhu, China"],"raw_orcid":"https://orcid.org/0000-0003-4987-0376","affiliations":[{"raw_affiliation_string":"School of Computer and Information, Anhui Normal University, Wuhu, China","institution_ids":["https://openalex.org/I4472751"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5016980869"],"corresponding_institution_ids":["https://openalex.org/I4472751"],"apc_list":null,"apc_paid":null,"fwci":3.8316,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":{"value":0.94307984,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"20","issue":null,"first_page":"1262","last_page":"1277"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11598","display_name":"Internet Traffic Analysis and Secure E-voting","score":0.9994000196456909,"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/T11598","display_name":"Internet Traffic Analysis and Secure E-voting","score":0.9994000196456909,"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/T10400","display_name":"Network Security and Intrusion Detection","score":0.9990000128746033,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9916999936103821,"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/undersampling","display_name":"Undersampling","score":0.9532338380813599},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8012517690658569},{"id":"https://openalex.org/keywords/intrusion-detection-system","display_name":"Intrusion detection system","score":0.660244882106781},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.499570369720459},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4488362669944763},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4216655492782593}],"concepts":[{"id":"https://openalex.org/C136536468","wikidata":"https://www.wikidata.org/wiki/Q1225894","display_name":"Undersampling","level":2,"score":0.9532338380813599},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8012517690658569},{"id":"https://openalex.org/C35525427","wikidata":"https://www.wikidata.org/wiki/Q745881","display_name":"Intrusion detection system","level":2,"score":0.660244882106781},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.499570369720459},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4488362669944763},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4216655492782593}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tifs.2024.3516547","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tifs.2024.3516547","pdf_url":null,"source":{"id":"https://openalex.org/S61310614","display_name":"IEEE Transactions on Information Forensics and Security","issn_l":"1556-6013","issn":["1556-6013","1556-6021"],"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 Transactions on Information Forensics and Security","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2843266738","display_name":null,"funder_award_id":"2022yf55","funder_id":"https://openalex.org/F4320336597","funder_display_name":"Key Research and Development Program of Wuhu"},{"id":"https://openalex.org/G315601948","display_name":null,"funder_award_id":"KJ2021ZD0007","funder_id":"https://openalex.org/F4320335406","funder_display_name":"University Natural Science Research Project of Anhui Province"},{"id":"https://openalex.org/G5660786734","display_name":null,"funder_award_id":"62306009","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G67807964","display_name":null,"funder_award_id":"62272006","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8908781066","display_name":null,"funder_award_id":"2023AH040026","funder_id":"https://openalex.org/F4320336601","funder_display_name":"Major Science and Technology Projects in Anhui Province"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335406","display_name":"University Natural Science Research Project of Anhui Province","ror":null},{"id":"https://openalex.org/F4320336597","display_name":"Key Research and Development Program of Wuhu","ror":null},{"id":"https://openalex.org/F4320336601","display_name":"Major Science and Technology Projects in Anhui Province","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":49,"referenced_works":["https://openalex.org/W1974647172","https://openalex.org/W2118978333","https://openalex.org/W2296509296","https://openalex.org/W2789828921","https://openalex.org/W2802643674","https://openalex.org/W2943045886","https://openalex.org/W2979824173","https://openalex.org/W3006921589","https://openalex.org/W3039681214","https://openalex.org/W3042621011","https://openalex.org/W3112044954","https://openalex.org/W3166992878","https://openalex.org/W3175175642","https://openalex.org/W3176639943","https://openalex.org/W3182158470","https://openalex.org/W3191470662","https://openalex.org/W3195419053","https://openalex.org/W3197690490","https://openalex.org/W3201788422","https://openalex.org/W3206325220","https://openalex.org/W3209696639","https://openalex.org/W3211470872","https://openalex.org/W3211494975","https://openalex.org/W3216652988","https://openalex.org/W4220818975","https://openalex.org/W4226135488","https://openalex.org/W4226249052","https://openalex.org/W4282047883","https://openalex.org/W4283790910","https://openalex.org/W4283816583","https://openalex.org/W4285140661","https://openalex.org/W4285166735","https://openalex.org/W4285184946","https://openalex.org/W4310863204","https://openalex.org/W4311167586","https://openalex.org/W4312429505","https://openalex.org/W4313591247","https://openalex.org/W4365795592","https://openalex.org/W4382203046","https://openalex.org/W4385062808","https://openalex.org/W4385568263","https://openalex.org/W4385767857","https://openalex.org/W4388505134","https://openalex.org/W4388938021","https://openalex.org/W6728757088","https://openalex.org/W6787801412","https://openalex.org/W6795916790","https://openalex.org/W6803975504","https://openalex.org/W6845133580"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W4387369504","https://openalex.org/W3046775127","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W3107602296","https://openalex.org/W4364306694","https://openalex.org/W4312192474"],"abstract_inverted_index":{"In":[0,249],"the":[1,5,10,18,51,55,112,116,119,136,142,147,151,183,190,195,202,208,213,226,234,237,263,273,281,285],"modern":[2],"interconnected":[3],"world,":[4],"popularization":[6],"of":[7,13,57,124,132,150,159,197,228,236,276,284],"networks":[8],"and":[9,22,80,122,153,206,232,240,279],"rapid":[11],"development":[12],"information":[14,241],"technology":[15],"led":[16],"to":[17,77,93,101,115,155,188,260],"increasing":[19],"security":[20,79,123],"risks":[21],"threats":[23],"in":[24,47,97,135,243,292],"network":[25,137],"systems.":[26],"The":[27,85,178],"existing":[28],"intrusion":[29,38,48,67,254],"detection":[30,68,148,204,255,270],"system":[31],"is":[32,89,129,180,287],"constantly":[33],"challenged":[34],"by":[35,107,141,289],"various":[36],"malicious":[37],"attacks.":[39],"Machine":[40],"learning":[41,87,171],"algorithms":[42],"have":[43],"been":[44],"widely":[45],"used":[46],"detection.":[49],"However,":[50,127],"model":[52,105,152,160,203,209,230,247,286],"training":[53,161,199,210,282],"requires":[54],"support":[56],"a":[58,103,130,156,167,268],"sufficient":[59],"high-quality":[60],"samples,":[61],"especially":[62,291],"attack":[63],"traffic":[64,138],"data.":[65],"Network":[66],"datasets":[69,256],"may":[70],"not":[71],"be":[72],"shared":[73,106],"between":[74],"organizations":[75,99],"due":[76],"data":[78,113,120,139,200,245],"some":[81],"privacy":[82,121],"policy":[83],"concerns.":[84],"federated":[86,169],"framework":[88,172,179],"an":[90],"optimal":[91],"approach":[92],"address":[94],"this":[95,164],"issue,":[96],"which":[98,144,192],"collaborate":[100],"train":[102],"global":[104,229],"multiple":[108],"parties":[109],"while":[110],"keeping":[111],"local":[114],"client,":[117,191],"guaranteeing":[118],"all":[125],"parties.":[126],"there":[128],"problem":[131],"class":[133,294],"imbalance":[134,295],"owned":[140],"organizations,":[143],"seriously":[145],"affects":[146],"performance":[149,205],"leads":[154],"high":[157],"consumption":[158],"time.":[162],"Therefore,":[163],"study":[165],"proposed":[166,264],"novel":[168],"undersampling":[170,186],"with":[173],"Gini":[174,221],"impurity,":[175],"namely":[176],"Fed-UGI.":[177],"based":[181,218],"on":[182,201,219,246,253,272],"hash-based":[184],"block":[185],"method":[187,266],"rebalance":[189],"can":[193,223],"solve":[194],"influence":[196],"imbalanced":[198],"improve":[207],"efficiency.":[211],"Moreover,":[212],"client":[214,244],"weighted":[215],"aggregation":[216,231],"strategy":[217],"Local":[220],"impurity":[222],"further":[224],"optimize":[225],"effect":[227,271],"reduce":[233],"impact":[235],"dispersion":[238],"degree":[239],"difference":[242],"aggregation.":[248],"addition,":[250],"extensive":[251],"experiments":[252],"show":[257],"that":[258],"compared":[259],"SOTA":[261],"methods,":[262],"Fed-UGI":[265],"has":[267],"good":[269],"three":[274],"metrics":[275],"F1-score,":[277],"G-mean":[278],"AUC,":[280],"time":[283],"reduced":[288],"51.76%-92.58%,":[290],"highly":[293],"situation.":[296]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":8}],"updated_date":"2026-05-15T08:27:34.491423","created_date":"2025-10-10T00:00:00"}