{"id":"https://openalex.org/W4415223187","doi":"https://doi.org/10.1109/cns66487.2025.11194963","title":"A Federated Flower Learning-Empowered Intrusion Detection System for Vehicle 6G Fog Networks","display_name":"A Federated Flower Learning-Empowered Intrusion Detection System for Vehicle 6G Fog Networks","publication_year":2025,"publication_date":"2025-09-08","ids":{"openalex":"https://openalex.org/W4415223187","doi":"https://doi.org/10.1109/cns66487.2025.11194963"},"language":"en","primary_location":{"id":"doi:10.1109/cns66487.2025.11194963","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cns66487.2025.11194963","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE Conference on Communications and Network Security (CNS)","raw_type":"proceedings-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/A5031220121","display_name":"Pattaraporn Khuwuthyakorn","orcid":"https://orcid.org/0009-0003-4993-949X"},"institutions":[{"id":"https://openalex.org/I4210155601","display_name":"Innovative Research (United States)","ror":"https://ror.org/04dec0k98","country_code":"US","type":"company","lineage":["https://openalex.org/I4210155601"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Pattaraporn Khuwuthyakorn","raw_affiliation_strings":["CMU,Innovative Research and Computational Science Lab,Thailand"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"CMU,Innovative Research and Computational Science Lab,Thailand","institution_ids":["https://openalex.org/I4210155601"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087374064","display_name":"Orawit Thinnukool","orcid":"https://orcid.org/0000-0002-1664-0059"},"institutions":[{"id":"https://openalex.org/I4210155601","display_name":"Innovative Research (United States)","ror":"https://ror.org/04dec0k98","country_code":"US","type":"company","lineage":["https://openalex.org/I4210155601"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Orawit Thinnukool","raw_affiliation_strings":["CMU,Innovative Research and Computational Science Lab,Thailand"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"CMU,Innovative Research and Computational Science Lab,Thailand","institution_ids":["https://openalex.org/I4210155601"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5022185985","display_name":"Abdullah Lakhan","orcid":"https://orcid.org/0000-0002-1833-1364"},"institutions":[{"id":"https://openalex.org/I343975184","display_name":"Dawood University of Engineering and Technology","ror":"https://ror.org/030xw6n96","country_code":"PK","type":"education","lineage":["https://openalex.org/I343975184"]}],"countries":["PK"],"is_corresponding":false,"raw_author_name":"Abdullah Lakhan","raw_affiliation_strings":["Dawood University of Eng. and Techn.,Department of Cybersecurity,Pakistan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dawood University of Eng. and Techn.,Department of Cybersecurity,Pakistan","institution_ids":["https://openalex.org/I343975184"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.25075744,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10148","display_name":"Advanced MIMO Systems Optimization","score":0.972599983215332,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10148","display_name":"Advanced MIMO Systems Optimization","score":0.972599983215332,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10761","display_name":"Vehicular Ad Hoc Networks (VANETs)","score":0.9465000033378601,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11932","display_name":"Wireless Body Area Networks","score":0.9169999957084656,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/testbed","display_name":"Testbed","score":0.7050999999046326},{"id":"https://openalex.org/keywords/intrusion-detection-system","display_name":"Intrusion detection system","score":0.651199996471405},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4507000148296356},{"id":"https://openalex.org/keywords/wireless-sensor-network","display_name":"Wireless sensor network","score":0.3950999975204468},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.38179999589920044},{"id":"https://openalex.org/keywords/data-transmission","display_name":"Data transmission","score":0.3702000081539154},{"id":"https://openalex.org/keywords/raw-data","display_name":"Raw data","score":0.367000013589859},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.36399999260902405},{"id":"https://openalex.org/keywords/edge-device","display_name":"Edge device","score":0.3488999903202057}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7285000085830688},{"id":"https://openalex.org/C31395832","wikidata":"https://www.wikidata.org/wiki/Q1318674","display_name":"Testbed","level":2,"score":0.7050999999046326},{"id":"https://openalex.org/C35525427","wikidata":"https://www.wikidata.org/wiki/Q745881","display_name":"Intrusion detection system","level":2,"score":0.651199996471405},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4507000148296356},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.42250001430511475},{"id":"https://openalex.org/C24590314","wikidata":"https://www.wikidata.org/wiki/Q336038","display_name":"Wireless sensor network","level":2,"score":0.3950999975204468},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.38179999589920044},{"id":"https://openalex.org/C557945733","wikidata":"https://www.wikidata.org/wiki/Q389772","display_name":"Data transmission","level":2,"score":0.3702000081539154},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.367000013589859},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.36399999260902405},{"id":"https://openalex.org/C138236772","wikidata":"https://www.wikidata.org/wiki/Q25098575","display_name":"Edge device","level":3,"score":0.3488999903202057},{"id":"https://openalex.org/C98025372","wikidata":"https://www.wikidata.org/wiki/Q477538","display_name":"Systems architecture","level":3,"score":0.34060001373291016},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.3248000144958496},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.32179999351501465},{"id":"https://openalex.org/C2778456923","wikidata":"https://www.wikidata.org/wiki/Q5337692","display_name":"Edge computing","level":3,"score":0.3190000057220459},{"id":"https://openalex.org/C110875604","wikidata":"https://www.wikidata.org/wiki/Q75","display_name":"The Internet","level":2,"score":0.3156999945640564},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.31540000438690186},{"id":"https://openalex.org/C2779808786","wikidata":"https://www.wikidata.org/wiki/Q6664603","display_name":"Locality","level":2,"score":0.3125},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2903999984264374},{"id":"https://openalex.org/C2779965156","wikidata":"https://www.wikidata.org/wiki/Q5227350","display_name":"Data sharing","level":3,"score":0.2888999879360199},{"id":"https://openalex.org/C90936777","wikidata":"https://www.wikidata.org/wiki/Q917189","display_name":"Host-based intrusion detection system","level":4,"score":0.28139999508857727},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.2784999907016754},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2777999937534332},{"id":"https://openalex.org/C2986652147","wikidata":"https://www.wikidata.org/wiki/Q21809931","display_name":"Fog computing","level":3,"score":0.27720001339912415},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2752000093460083},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.2728999853134155},{"id":"https://openalex.org/C158251709","wikidata":"https://www.wikidata.org/wiki/Q354025","display_name":"Intrusion","level":2,"score":0.2727999985218048},{"id":"https://openalex.org/C123201435","wikidata":"https://www.wikidata.org/wiki/Q456632","display_name":"Information privacy","level":2,"score":0.26649999618530273},{"id":"https://openalex.org/C81860439","wikidata":"https://www.wikidata.org/wiki/Q251212","display_name":"Internet of Things","level":2,"score":0.2542000114917755},{"id":"https://openalex.org/C93996380","wikidata":"https://www.wikidata.org/wiki/Q44127","display_name":"Server","level":2,"score":0.2515999972820282}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cns66487.2025.11194963","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cns66487.2025.11194963","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE Conference on Communications and Network Security (CNS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W4285733873","https://openalex.org/W4318586189","https://openalex.org/W4385210255","https://openalex.org/W4391216281","https://openalex.org/W4392567270","https://openalex.org/W4393104961","https://openalex.org/W4401539193","https://openalex.org/W4406612286","https://openalex.org/W4409700807","https://openalex.org/W4410638774"],"related_works":[],"abstract_inverted_index":{"The":[0,58,93,121,206,228],"evolution":[1],"of":[2,9,76,83,223],"intelligent":[3],"transportation":[4],"systems":[5],"in":[6,15,215,220,245,282],"the":[7,44,63,159,176,194,221,254,271],"era":[8],"6G":[10,40,285],"has":[11],"introduced":[12],"new":[13],"challenges":[14],"cybersecurity,":[16],"particularly":[17],"for":[18,38],"vehicular":[19,151],"networks":[20],"interconnected":[21],"through":[22],"fog":[23,87,117],"and":[24,66,78,90,98,107,118,139,147,183,217,238,260,269,278],"edge":[25],"nodes.":[26],"This":[27,192],"research":[28],"proposes":[29],"a":[30,73,110,125,154,171,189],"Federated":[31],"Flower":[32,160],"Learning-Empowered":[33],"Intrusion":[34],"Detection":[35],"System":[36],"(IDS)":[37],"Ve-hicle":[39],"Fog":[41],"Networks":[42],"using":[43,158],"TON_loT":[45,59],"dataset,":[46,60],"aiming":[47],"to":[48,135,196,210,264],"achieve":[49],"high-accuracy,":[50],"low-latency":[51],"attack":[52],"detection":[53,236],"while":[54,201],"preserving":[55],"data":[56,80,145,182,200,214],"privacy.":[57],"collected":[61],"from":[62,81,275],"Cyber":[64],"Range":[65],"loT":[67,119],"Labs":[68],"at":[69],"UNSW":[70],"Canberra,":[71],"offers":[72],"diverse":[74],"range":[75],"telemetry":[77],"system":[79,207,256],"Internet":[82],"Things":[84],"(loT)":[85],"sensors,":[86],"operating":[88],"systems,":[89],"network":[91],"traffic.":[92],"dataset":[94,134],"encompasses":[95],"both":[96,276],"benign":[97],"malicious":[99,226,280],"behaviors,":[100],"including":[101],"attacks":[102],"such":[103],"as":[104,170],"DoS,":[105],"DDoS,":[106],"ransomware,":[108],"within":[109],"realistic":[111],"Industry":[112],"4.0":[113],"testbed":[114],"comprising":[115],"virtualized":[116],"environments.":[120],"proposed":[122,255],"IDS":[123,177,233,267],"leverages":[124],"Convolutional":[126],"Neural":[127],"Network":[128],"(CNN)":[129],"trained":[130],"on":[131,179],"this":[132,165],"rich":[133],"extract":[136],"spatial":[137],"features":[138],"detect":[140],"intrusion":[141],"patterns.":[142],"To":[143],"maintain":[144],"locality":[146],"privacy":[148],"across":[149],"distributed":[150,283],"sensor":[152,181,213],"nodes,":[153],"federated":[155,172,229],"learning":[156],"architecture":[157],"framework":[161],"is":[162,208],"implemented.":[163],"In":[164],"architecture,":[166],"each":[167],"vehicle":[168,247,284],"acts":[169],"client,":[173],"locally":[174],"training":[175],"model":[178,186,204],"its":[180],"sharing":[184],"only":[185],"updates":[187],"with":[188],"central":[190],"aggregator.":[191],"eliminates":[193],"need":[195],"transfer":[197],"raw":[198],"sensitive":[199],"ensuring":[202,242],"collaborative":[203],"refinement.":[205],"designed":[209],"monitor":[211],"in-vehicle":[212],"real-time":[216],"raise":[218],"alerts":[219],"presence":[222],"anomalies":[224],"or":[225],"activity.":[227],"CNN":[230],"-":[231],"based":[232],"achieves":[234],"high":[235],"accuracy":[237],"minimises":[239],"end-to-end":[240],"latency,":[241],"timely":[243],"responses":[244],"critical":[246],"control":[248],"scenarios.":[249],"Simulation":[250],"results":[251],"show":[252],"that":[253],"reduces":[257],"bandwidth":[258],"consumption":[259],"response":[261],"delays":[262],"compared":[263],"traditional":[265],"cloud-based":[266],"solutions":[268],"identifies":[270],"highest":[272],"security":[273],"risk":[274],"known":[277],"unknown":[279],"threats":[281],"networks.":[286]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-16T00:00:00"}