{"id":"https://openalex.org/W4315630381","doi":"https://doi.org/10.1109/paap56126.2022.10010553","title":"Cluster-based Federated Learning Framework for Intrusion Detection","display_name":"Cluster-based Federated Learning Framework for Intrusion Detection","publication_year":2022,"publication_date":"2022-11-25","ids":{"openalex":"https://openalex.org/W4315630381","doi":"https://doi.org/10.1109/paap56126.2022.10010553"},"language":"en","primary_location":{"id":"doi:10.1109/paap56126.2022.10010553","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/paap56126.2022.10010553","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE 13th International Symposium on Parallel Architectures, Algorithms and Programming (PAAP)","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/A5036192863","display_name":"Luxin Cai","orcid":"https://orcid.org/0009-0003-5132-1429"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Luxin Cai","raw_affiliation_strings":["Beijing Jiaotong University,School of Computer and Information Technology,Beijing,China","School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Jiaotong University,School of Computer and Information Technology,Beijing,China","institution_ids":["https://openalex.org/I21193070"]},{"raw_affiliation_string":"School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049441487","display_name":"Naiyue Chen","orcid":"https://orcid.org/0000-0001-9681-9405"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Naiyue Chen","raw_affiliation_strings":["Beijing Jiaotong University,School of Computer and Information Technology,Beijing,China","School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Jiaotong University,School of Computer and Information Technology,Beijing,China","institution_ids":["https://openalex.org/I21193070"]},{"raw_affiliation_string":"School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053600526","display_name":"Yuanmeng Wei","orcid":null},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuanmeng Wei","raw_affiliation_strings":["Beijing Jiaotong University,School of Computer and Information Technology,Beijing,China","School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Jiaotong University,School of Computer and Information Technology,Beijing,China","institution_ids":["https://openalex.org/I21193070"]},{"raw_affiliation_string":"School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040104068","display_name":"Huaping Chen","orcid":"https://orcid.org/0000-0002-2058-7086"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huaping Chen","raw_affiliation_strings":["Qi&#x2019;anxin Technology Group Co.,Ltd..,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Qi&#x2019;anxin Technology Group Co.,Ltd..,Beijing,China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5010019122","display_name":"Yidong Li","orcid":"https://orcid.org/0000-0003-2965-6196"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yidong Li","raw_affiliation_strings":["Beijing Jiaotong University,School of Computer and Information Technology,Beijing,China","School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing Jiaotong University,School of Computer and Information Technology,Beijing,China","institution_ids":["https://openalex.org/I21193070"]},{"raw_affiliation_string":"School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5036192863"],"corresponding_institution_ids":["https://openalex.org/I21193070"],"apc_list":null,"apc_paid":null,"fwci":0.8288,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.74894759,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"2","issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10400","display_name":"Network Security and Intrusion Detection","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9998000264167786,"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":0.9977999925613403,"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":0.851130485534668},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.7239367365837097},{"id":"https://openalex.org/keywords/intrusion-detection-system","display_name":"Intrusion detection system","score":0.7019052505493164},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.6350217461585999},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.5859405994415283},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.557819664478302},{"id":"https://openalex.org/keywords/the-internet","display_name":"The Internet","score":0.5433838963508606},{"id":"https://openalex.org/keywords/cluster","display_name":"Cluster (spacecraft)","score":0.511214017868042},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.36325085163116455},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2490168809890747},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2108817994594574},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.134403258562088}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.851130485534668},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7239367365837097},{"id":"https://openalex.org/C35525427","wikidata":"https://www.wikidata.org/wiki/Q745881","display_name":"Intrusion detection system","level":2,"score":0.7019052505493164},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.6350217461585999},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.5859405994415283},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.557819664478302},{"id":"https://openalex.org/C110875604","wikidata":"https://www.wikidata.org/wiki/Q75","display_name":"The Internet","level":2,"score":0.5433838963508606},{"id":"https://openalex.org/C164866538","wikidata":"https://www.wikidata.org/wiki/Q367351","display_name":"Cluster (spacecraft)","level":2,"score":0.511214017868042},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.36325085163116455},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2490168809890747},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2108817994594574},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.134403258562088},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/paap56126.2022.10010553","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/paap56126.2022.10010553","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE 13th International Symposium on Parallel Architectures, Algorithms and Programming (PAAP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.46000000834465027}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W2296509296","https://openalex.org/W2912213068","https://openalex.org/W2921453769","https://openalex.org/W2971212725","https://openalex.org/W2986055611","https://openalex.org/W3033403042","https://openalex.org/W3040552057","https://openalex.org/W3045229347","https://openalex.org/W3086579950","https://openalex.org/W3093410479","https://openalex.org/W3123459983","https://openalex.org/W3128470182","https://openalex.org/W3210361010"],"related_works":["https://openalex.org/W2357468538","https://openalex.org/W1577110157","https://openalex.org/W2355007334","https://openalex.org/W4298130764","https://openalex.org/W2390009783","https://openalex.org/W2804364458","https://openalex.org/W4254602698","https://openalex.org/W2394461323","https://openalex.org/W2349441905","https://openalex.org/W2361044160"],"abstract_inverted_index":{"With":[0],"the":[1,7,16,24,28,58,69,78,107,110,117,122],"rapid":[2],"development":[3],"of":[4,31,49,71,113,175],"Industrial":[5,17],"Internet,":[6,18],"network":[8,32,91],"intrusion":[9,33,92],"detection":[10,34],"has":[11,39,159],"become":[12,40],"particularly":[13],"important.":[14],"In":[15,94],"large-scale":[19],"data":[20,47,59,80,104,114,176,180],"is":[21,61],"distributed":[22],"in":[23,116,170],"edge":[25,37,65],"nodes":[26,51],"caused":[27],"joint":[29],"analysis":[30],"at":[35],"each":[36,64],"node":[38],"necessary.":[41],"Federated":[42],"learning":[43,73,88,128],"structure":[44],"can":[45,167],"avoid":[46],"out":[48],"local":[50],"to":[52],"protect":[53],"user":[54],"privacy":[55,183],"data.":[56],"However,":[57],"distribution":[60],"different":[62,101,173],"for":[63,90],"nodes,":[66],"which":[67,106],"limits":[68],"effectiveness":[70],"federated":[72,87,127],"models.":[74],"We":[75,145],"focus":[76],"on":[77,121,150],"non-IID":[79],"features":[81],"and":[82,134,142,164,182],"propose":[83],"a":[84],"new":[85],"cluster-based":[86],"framework":[89],"detection.":[93],"this":[95],"method,":[96],"we":[97,125],"cluster":[98,132],"clients":[99,108],"into":[100,131],"communities":[102],"by":[103,137],"labels,":[105],"contain":[109],"similar":[111],"proportion":[112],"labels":[115],"same":[118],"community.":[119],"Based":[120],"clustering":[123],"results,":[124],"decompose":[126],"model":[129],"aggregation":[130,133,136],"global":[135],"leveraging":[138],"similarities":[139],"both":[140],"within":[141],"between":[143],"clusters.":[144],"conduct":[146],"extensive":[147],"experiments":[148],"based":[149],"UNSW_NB15":[151],"dataset.":[152],"The":[153],"results":[154],"show":[155],"that":[156],"our":[157],"method":[158],"better":[160],"performance":[161],"than":[162],"FedAvg":[163],"FedProx.":[165],"It":[166],"work":[168],"well":[169],"scenarios":[171],"with":[172],"distributions":[174],"samples":[177],"while":[178],"ensuring":[179],"security":[181],"protection.":[184]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":1}],"updated_date":"2025-12-19T19:40:27.379048","created_date":"2025-10-10T00:00:00"}
