{"id":"https://openalex.org/W2994688391","doi":"https://doi.org/10.1145/3368926.3369705","title":"Multi-Task Network Anomaly Detection using Federated Learning","display_name":"Multi-Task Network Anomaly Detection using Federated Learning","publication_year":2019,"publication_date":"2019-01-01","ids":{"openalex":"https://openalex.org/W2994688391","doi":"https://doi.org/10.1145/3368926.3369705","mag":"2994688391"},"language":"en","primary_location":{"is_oa":false,"landing_page_url":"https://doi.org/10.1145/3368926.3369705","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false},"type":"article","type_crossref":"proceedings-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/A5101511740","display_name":"Ying Zhao","orcid":"https://orcid.org/0000-0002-4113-8423"},"institutions":[{"id":"https://openalex.org/I75390827","display_name":"Beijing University of Chemical Technology","ror":"https://ror.org/00df5yc52","country_code":"CN","type":"education","lineage":["https://openalex.org/I75390827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ying Zhao","raw_affiliation_strings":["Beijing University of Chemical Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Chemical Technology, Beijing, China","institution_ids":["https://openalex.org/I75390827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100785999","display_name":"Junjun Chen","orcid":"https://orcid.org/0000-0001-8902-2553"},"institutions":[{"id":"https://openalex.org/I75390827","display_name":"Beijing University of Chemical Technology","ror":"https://ror.org/00df5yc52","country_code":"CN","type":"education","lineage":["https://openalex.org/I75390827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junjun Chen","raw_affiliation_strings":["Beijing University of Chemical Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Chemical Technology, Beijing, China","institution_ids":["https://openalex.org/I75390827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100599890","display_name":"Di Wu","orcid":"https://orcid.org/0000-0002-4753-8161"},"institutions":[{"id":"https://openalex.org/I114017466","display_name":"University of Technology Sydney","ror":"https://ror.org/03f0f6041","country_code":"AU","type":"education","lineage":["https://openalex.org/I114017466"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Di Wu","raw_affiliation_strings":["University of Technology Sydney, Sydney, Australia"],"affiliations":[{"raw_affiliation_string":"University of Technology Sydney, Sydney, Australia","institution_ids":["https://openalex.org/I114017466"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101001849","display_name":"Jian Teng","orcid":null},"institutions":[{"id":"https://openalex.org/I75390827","display_name":"Beijing University of Chemical Technology","ror":"https://ror.org/00df5yc52","country_code":"CN","type":"education","lineage":["https://openalex.org/I75390827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jian Teng","raw_affiliation_strings":["Beijing University of Chemical Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Chemical Technology, Beijing, China","institution_ids":["https://openalex.org/I75390827"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5005228053","display_name":"Shui Yu","orcid":"https://orcid.org/0000-0003-4485-6743"},"institutions":[{"id":"https://openalex.org/I114017466","display_name":"University of Technology Sydney","ror":"https://ror.org/03f0f6041","country_code":"AU","type":"education","lineage":["https://openalex.org/I114017466"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Shui Yu","raw_affiliation_strings":["University of Technology Sydney, Sydney, Australia"],"affiliations":[{"raw_affiliation_string":"University of Technology Sydney, Sydney, Australia","institution_ids":["https://openalex.org/I114017466"]}]}],"institution_assertions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":11.718,"has_fulltext":false,"cited_by_count":118,"citation_normalized_percentile":{"value":0.873681,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"273","last_page":"279"},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T11598","display_name":"Machine Learning for Internet Traffic Classification","score":0.9999,"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":"Machine Learning for Internet Traffic Classification","score":0.9999,"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 Intrusion Detection and Defense Mechanisms","score":0.9997,"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/T10764","display_name":"Privacy-Preserving Techniques for Data Analysis and Machine Learning","score":0.9974,"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/anomaly-detection","display_name":"Anomaly Detection","score":0.529858},{"id":"https://openalex.org/keywords/outlier-detection","display_name":"Outlier Detection","score":0.522005},{"id":"https://openalex.org/keywords/botnet-detection","display_name":"Botnet Detection","score":0.508635},{"id":"https://openalex.org/keywords/traffic-analysis","display_name":"Traffic Analysis","score":0.506365},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.42783067}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.822953},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.7150036},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.6681495},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.62285995},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5177514},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.51048934},{"id":"https://openalex.org/C28006648","wikidata":"https://www.wikidata.org/wiki/Q6934509","display_name":"Multi-task learning","level":3,"score":0.48305827},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.47533536},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.46575576},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.46520525},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.42783067},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.41105986},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.38109204},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.08555618},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0},{"id":"https://openalex.org/C111368507","wikidata":"https://www.wikidata.org/wiki/Q43518","display_name":"Oceanography","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C153349607","wikidata":"https://www.wikidata.org/wiki/Q36649","display_name":"Visual arts","level":1,"score":0.0},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"is_oa":false,"landing_page_url":"https://doi.org/10.1145/3368926.3369705","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false}],"best_oa_location":null,"sustainable_development_goals":[],"grants":[],"datasets":[],"versions":[],"referenced_works_count":23,"referenced_works":["https://openalex.org/W1978502884","https://openalex.org/W2053637704","https://openalex.org/W2079411451","https://openalex.org/W2161251616","https://openalex.org/W2343828539","https://openalex.org/W2346714907","https://openalex.org/W2426032775","https://openalex.org/W2476429474","https://openalex.org/W2527999453","https://openalex.org/W2560162835","https://openalex.org/W2591712613","https://openalex.org/W2766315530","https://openalex.org/W2783741806","https://openalex.org/W2789828921","https://openalex.org/W2794826941","https://openalex.org/W2889836475","https://openalex.org/W2908941882","https://openalex.org/W2912213068","https://openalex.org/W2921165530","https://openalex.org/W2950745363","https://openalex.org/W2963197901","https://openalex.org/W2964121744","https://openalex.org/W2966474135"],"related_works":["https://openalex.org/W4296359239","https://openalex.org/W2913146933","https://openalex.org/W2383111961","https://openalex.org/W2380820513","https://openalex.org/W2372385138","https://openalex.org/W2365952365","https://openalex.org/W2363545964","https://openalex.org/W2352448290","https://openalex.org/W2101155126","https://openalex.org/W2043093291"],"abstract_inverted_index":{"Because":[0],"of":[1,4,19,26,87,96],"the":[2,13,20,24,56,69,77,88,101,147,166,173,179],"complexity":[3],"network":[5,14,112,121,128],"traffic,":[6],"there":[7],"are":[8],"various":[9],"significant":[10],"challenges":[11,22],"in":[12,71,122,182],"anomaly":[15,97,129],"detection":[16,130,167],"fields.":[17],"One":[18],"major":[21],"is":[23,176],"lack":[25],"labeled":[27],"training":[28,58,67,78,152,184],"data.":[29],"In":[30],"this":[31],"paper,":[32],"we":[33,115],"use":[34],"federated":[35,72,123],"learning":[36,124],"to":[37,43,64,68,111,126],"tackle":[38],"data":[39,45,79],"scarcity":[40],"problem":[41],"and":[42,104,137,161,168],"preserve":[44],"privacy,":[46],"where":[47],"multiple":[48,144],"participants":[49,60],"collaboratively":[50],"train":[51],"a":[52,117],"global":[53],"model.":[54],"Unlike":[55],"centralized":[57,183],"architecture,":[59],"do":[61],"not":[62,106],"need":[63],"share":[65],"their":[66],"server":[70],"learning,":[73],"which":[74,99],"can":[75,105,150],"prevent":[76],"from":[80],"being":[81],"exploited":[82],"by":[83,172],"attackers.":[84],"Moreover,":[85],"most":[86],"previous":[89],"works":[90],"focus":[91],"on":[92,157],"one":[93],"specific":[94],"task":[95],"detection,":[98],"restricts":[100],"application":[102],"areas":[103],"provide":[107],"more":[108],"valuable":[109],"information":[110],"administrators.":[113],"Therefore,":[114],"propose":[116],"multi-task":[118,148],"deep":[119],"neural":[120],"(MT-DNN-FL)":[125],"perform":[127],"task,":[131,136,140],"VPN":[132],"(Tor)":[133],"traffic":[134,138],"recognition":[135],"classification":[139,169],"simultaneously.":[141],"Compared":[142],"with":[143],"single-task":[145],"models,":[146],"method":[149,175],"reduce":[151],"time":[153],"overhead.":[154],"Experiments":[155],"conducted":[156],"well-known":[158],"CICIDS2017,":[159],"ISCXVPN2016,":[160],"ISCXTor2016":[162],"datasets,":[163],"show":[164],"that":[165],"performance":[170],"achieved":[171],"proposed":[174],"better":[177],"than":[178],"baseline":[180],"methods":[181],"architecture.":[185]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W2994688391","counts_by_year":[{"year":2024,"cited_by_count":22},{"year":2023,"cited_by_count":33},{"year":2022,"cited_by_count":22},{"year":2021,"cited_by_count":23},{"year":2020,"cited_by_count":15}],"updated_date":"2024-12-03T18:08:23.387561","created_date":"2019-12-26"}
