{"id":"https://openalex.org/W4406267124","doi":"https://doi.org/10.1109/vtc2024-fall63153.2024.10757488","title":"An Efficient Federated Learning Framework for IoT Intrusion Detection","display_name":"An Efficient Federated Learning Framework for IoT Intrusion Detection","publication_year":2024,"publication_date":"2024-10-07","ids":{"openalex":"https://openalex.org/W4406267124","doi":"https://doi.org/10.1109/vtc2024-fall63153.2024.10757488"},"language":"en","primary_location":{"id":"doi:10.1109/vtc2024-fall63153.2024.10757488","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vtc2024-fall63153.2024.10757488","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE 100th Vehicular Technology Conference (VTC2024-Fall)","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/A5038811546","display_name":"Yushen Chen","orcid":"https://orcid.org/0009-0009-2071-5955"},"institutions":[{"id":"https://openalex.org/I125749732","display_name":"Western University","ror":"https://ror.org/02grkyz14","country_code":"CA","type":"education","lineage":["https://openalex.org/I125749732"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Yushen Chen","raw_affiliation_strings":["Western University,Department of Computer Science,London,Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Western University,Department of Computer Science,London,Canada","institution_ids":["https://openalex.org/I125749732"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100386867","display_name":"Fang Fang","orcid":"https://orcid.org/0000-0002-6582-6570"},"institutions":[{"id":"https://openalex.org/I125749732","display_name":"Western University","ror":"https://ror.org/02grkyz14","country_code":"CA","type":"education","lineage":["https://openalex.org/I125749732"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Fang Fang","raw_affiliation_strings":["Western University,Department of Computer Science,London,Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Western University,Department of Computer Science,London,Canada","institution_ids":["https://openalex.org/I125749732"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100383955","display_name":"Boyu Wang","orcid":"https://orcid.org/0000-0002-7413-4162"},"institutions":[{"id":"https://openalex.org/I125749732","display_name":"Western University","ror":"https://ror.org/02grkyz14","country_code":"CA","type":"education","lineage":["https://openalex.org/I125749732"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Boyu Wang","raw_affiliation_strings":["Western University,Department of Computer Science,London,Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Western University,Department of Computer Science,London,Canada","institution_ids":["https://openalex.org/I125749732"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100322308","display_name":"Lan Zhang","orcid":"https://orcid.org/0000-0002-7144-9089"},"institutions":[{"id":"https://openalex.org/I8078737","display_name":"Clemson University","ror":"https://ror.org/037s24f05","country_code":"US","type":"education","lineage":["https://openalex.org/I8078737"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lan Zhang","raw_affiliation_strings":["Clemson University,Department of Electrical and Computer Engineering,Clemson,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Clemson University,Department of Electrical and Computer Engineering,Clemson,USA","institution_ids":["https://openalex.org/I8078737"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.6755,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.84891971,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10400","display_name":"Network Security and Intrusion Detection","score":0.9969000220298767,"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":0.9969000220298767,"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/T11598","display_name":"Internet Traffic Analysis and Secure E-voting","score":0.9765999913215637,"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/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9724000096321106,"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/computer-science","display_name":"Computer science","score":0.8226230144500732},{"id":"https://openalex.org/keywords/intrusion-detection-system","display_name":"Intrusion detection system","score":0.6608288884162903},{"id":"https://openalex.org/keywords/internet-of-things","display_name":"Internet of Things","score":0.6141639351844788},{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.522901713848114},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.3267418146133423},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.25792786478996277},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.2535971999168396}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8226230144500732},{"id":"https://openalex.org/C35525427","wikidata":"https://www.wikidata.org/wiki/Q745881","display_name":"Intrusion detection system","level":2,"score":0.6608288884162903},{"id":"https://openalex.org/C81860439","wikidata":"https://www.wikidata.org/wiki/Q251212","display_name":"Internet of Things","level":2,"score":0.6141639351844788},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.522901713848114},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.3267418146133423},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.25792786478996277},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.2535971999168396}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/vtc2024-fall63153.2024.10757488","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vtc2024-fall63153.2024.10757488","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE 100th Vehicular Technology Conference (VTC2024-Fall)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320334593","display_name":"Natural Sciences and Engineering Research Council of Canada","ror":"https://ror.org/01h531d29"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W4298221930","https://openalex.org/W4245926026","https://openalex.org/W4311097251","https://openalex.org/W2586548817","https://openalex.org/W2390279801","https://openalex.org/W2777914285","https://openalex.org/W3176937389"],"abstract_inverted_index":{"The":[0,180],"exponential":[1],"growth":[2],"of":[3,6,54,68,85,108,118,162,170,176,193],"the":[4,51,82,104,114,119,144,152,159,163,167,174,191,194],"Internet":[5],"Things":[7],"(IoT)":[8],"ecosystems":[9],"has":[10],"raised":[11],"significant":[12],"cybersecurity":[13],"concerns.":[14],"Deep":[15],"learning":[16,58,63],"(DL)-based":[17],"methods":[18,34],"have":[19],"shown":[20],"promising":[21],"performance":[22],"in":[23,28],"detecting":[24],"potential":[25],"cyber":[26],"threats":[27],"IoT":[29,47,69,79,86,109,164],"networks.":[30,56],"However,":[31,81],"as":[32,60,95],"these":[33,124],"often":[35],"involve":[36],"data":[37,43,105,178],"centralization,":[38],"they":[39],"can":[40,88,111],"pose":[41],"serious":[42],"privacy":[44],"issues":[45,107],"for":[46],"users":[48],"and":[49,99,116,138,172,189],"increase":[50],"communication":[52,168],"burden":[53],"local":[55],"Federated":[57],"(FL),":[59],"a":[61,148],"distributed":[62],"paradigm,":[64],"enables":[65],"privacy-preserving":[66],"training":[67,92,97],"intrusion":[70,196],"detection":[71,197],"models":[72],"by":[73],"requiring":[74],"only":[75],"model":[76],"updates":[77],"from":[78],"devices.":[80],"resource-constrained":[83],"nature":[84],"devices":[87,110],"significantly":[89],"decrease":[90],"FL":[91,130],"efficiencies,":[93],"such":[94],"increased":[96],"latency":[98],"delayed":[100],"convergence":[101],"speed.":[102],"Moreover,":[103],"heterogeneous":[106,177],"also":[112],"impact":[113,175],"accuracy":[115,146,188],"robustness":[117,192],"trained":[120,195],"model.":[121,198],"To":[122,142],"address":[123],"challenges,":[125],"we":[126],"propose":[127],"an":[128],"efficient":[129],"framework,":[131],"FedKD-Prox,":[132],"based":[133],"on":[134],"federated":[135],"proximal":[136],"(FedProx)":[137],"knowledge":[139],"distillation":[140],"(KD).":[141],"improve":[143],"prediction":[145],"within":[147],"limited":[149],"time":[150],"budget,":[151],"proposed":[153],"framework":[154],"aims":[155],"to":[156],"efficiently":[157],"exploit":[158],"computation":[160],"capability":[161],"trainers,":[165],"reduce":[166],"overhead":[169],"FL,":[171],"alleviate":[173],"issues.":[179],"simulation":[181],"results":[182],"show":[183],"that":[184],"FedKD-Prox":[185],"achieves":[186],"higher":[187],"improves":[190]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}