{"id":"https://openalex.org/W3089378999","doi":"https://doi.org/10.1109/icccn49398.2020.9209704","title":"Practical and White-Box Anomaly Detection through Unsupervised and Active Learning","display_name":"Practical and White-Box Anomaly Detection through Unsupervised and Active Learning","publication_year":2020,"publication_date":"2020-08-01","ids":{"openalex":"https://openalex.org/W3089378999","doi":"https://doi.org/10.1109/icccn49398.2020.9209704","mag":"3089378999"},"language":"en","primary_location":{"id":"doi:10.1109/icccn49398.2020.9209704","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icccn49398.2020.9209704","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 29th International Conference on Computer Communications and Networks (ICCCN)","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/A5100318954","display_name":"Yao Wang","orcid":"https://orcid.org/0000-0001-6735-9622"},"institutions":[{"id":"https://openalex.org/I29955533","display_name":"Center for Information Technology","ror":"https://ror.org/03jh5a977","country_code":"US","type":"facility","lineage":["https://openalex.org/I1299022934","https://openalex.org/I1299303238","https://openalex.org/I29955533"]},{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN","US"],"is_corresponding":false,"raw_author_name":"Yao Wang","raw_affiliation_strings":["Beijing National Research Center for Information Science and Technology (BNRist)","Tsinghua University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing National Research Center for Information Science and Technology (BNRist)","institution_ids":["https://openalex.org/I29955533"]},{"raw_affiliation_string":"Tsinghua University","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100655132","display_name":"Zhaowei Wang","orcid":"https://orcid.org/0000-0002-7797-3316"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhaowei Wang","raw_affiliation_strings":["BizSeer"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"BizSeer","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043412621","display_name":"Zejun Xie","orcid":"https://orcid.org/0000-0001-5938-4828"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zejun Xie","raw_affiliation_strings":["BizSeer"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"BizSeer","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102769427","display_name":"Nengwen Zhao","orcid":"https://orcid.org/0000-0002-5729-0884"},"institutions":[{"id":"https://openalex.org/I29955533","display_name":"Center for Information Technology","ror":"https://ror.org/03jh5a977","country_code":"US","type":"facility","lineage":["https://openalex.org/I1299022934","https://openalex.org/I1299303238","https://openalex.org/I29955533"]},{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN","US"],"is_corresponding":false,"raw_author_name":"Nengwen Zhao","raw_affiliation_strings":["Beijing National Research Center for Information Science and Technology (BNRist)","Tsinghua University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing National Research Center for Information Science and Technology (BNRist)","institution_ids":["https://openalex.org/I29955533"]},{"raw_affiliation_string":"Tsinghua University","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100365555","display_name":"Junjie Chen","orcid":"https://orcid.org/0000-0003-3056-9962"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junjie Chen","raw_affiliation_strings":["Tianjin University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tianjin University","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101581273","display_name":"Wenchi Zhang","orcid":"https://orcid.org/0000-0002-5599-030X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wenchi Zhang","raw_affiliation_strings":["BizSeer"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"BizSeer","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063543200","display_name":"Kaixin Sui","orcid":"https://orcid.org/0000-0003-4545-7621"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kaixin Sui","raw_affiliation_strings":["BizSeer"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"BizSeer","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5046419834","display_name":"Dan Pei","orcid":"https://orcid.org/0000-0002-5113-838X"},"institutions":[{"id":"https://openalex.org/I29955533","display_name":"Center for Information Technology","ror":"https://ror.org/03jh5a977","country_code":"US","type":"facility","lineage":["https://openalex.org/I1299022934","https://openalex.org/I1299303238","https://openalex.org/I29955533"]},{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN","US"],"is_corresponding":false,"raw_author_name":"Dan Pei","raw_affiliation_strings":["Beijing National Research Center for Information Science and Technology (BNRist)","Tsinghua University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing National Research Center for Information Science and Technology (BNRist)","institution_ids":["https://openalex.org/I29955533"]},{"raw_affiliation_string":"Tsinghua University","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.725,"has_fulltext":false,"cited_by_count":16,"citation_normalized_percentile":{"value":0.87834917,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"43","issue":null,"first_page":"1","last_page":"9"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":1.0,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":1.0,"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":0.9994999766349792,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9907000064849854,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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":0.7882207632064819},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7377219200134277},{"id":"https://openalex.org/keywords/performance-indicator","display_name":"Performance indicator","score":0.5781214237213135},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5287327766418457},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5245404839515686},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.47404858469963074},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.4609658420085907},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.43674683570861816},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4301314651966095},{"id":"https://openalex.org/keywords/active-learning","display_name":"Active learning (machine learning)","score":0.4200953543186188}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.7882207632064819},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7377219200134277},{"id":"https://openalex.org/C135510737","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Performance indicator","level":2,"score":0.5781214237213135},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5287327766418457},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5245404839515686},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.47404858469963074},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.4609658420085907},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.43674683570861816},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4301314651966095},{"id":"https://openalex.org/C77967617","wikidata":"https://www.wikidata.org/wiki/Q4677561","display_name":"Active learning (machine learning)","level":2,"score":0.4200953543186188},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","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/C26873012","wikidata":"https://www.wikidata.org/wiki/Q214781","display_name":"Condensed matter physics","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/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icccn49398.2020.9209704","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icccn49398.2020.9209704","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 29th International Conference on Computer Communications and Networks (ICCCN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","score":0.4099999964237213,"display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W116902681","https://openalex.org/W197787652","https://openalex.org/W1971022913","https://openalex.org/W2026453187","https://openalex.org/W2049058890","https://openalex.org/W2093606067","https://openalex.org/W2097747115","https://openalex.org/W2164099512","https://openalex.org/W2166277028","https://openalex.org/W2278984902","https://openalex.org/W2296719434","https://openalex.org/W2323958325","https://openalex.org/W2340896621","https://openalex.org/W2345006530","https://openalex.org/W2472119793","https://openalex.org/W2555676925","https://openalex.org/W2667207928","https://openalex.org/W2785362611","https://openalex.org/W2794090615","https://openalex.org/W2809108362","https://openalex.org/W2903158431","https://openalex.org/W2912126994","https://openalex.org/W2918465401","https://openalex.org/W2919841582","https://openalex.org/W2928232427","https://openalex.org/W2944981198","https://openalex.org/W2946491813","https://openalex.org/W2947096836","https://openalex.org/W2997116778","https://openalex.org/W3028340924","https://openalex.org/W3098957257","https://openalex.org/W4293417080","https://openalex.org/W6604828220","https://openalex.org/W6608049307","https://openalex.org/W6684359833","https://openalex.org/W6720330311","https://openalex.org/W6756615331"],"related_works":["https://openalex.org/W2806741695","https://openalex.org/W4290647774","https://openalex.org/W3189286258","https://openalex.org/W3207797160","https://openalex.org/W3210364259","https://openalex.org/W4300558037","https://openalex.org/W2912112202","https://openalex.org/W2667207928","https://openalex.org/W4377864969","https://openalex.org/W3030345572"],"abstract_inverted_index":{"To":[0],"ensure":[1],"quality":[2],"of":[3,18,39,45,60,121,135],"service":[4],"and":[5,27,42,70,92,103,159,185,201],"user":[6],"experience,":[7],"large":[8,37,166],"Internet":[9],"companies":[10],"often":[11],"monitor":[12],"various":[13,40],"Key":[14],"Performance":[15],"Indicators":[16],"(KPIs)":[17],"their":[19],"systems":[20],"so":[21],"that":[22,173],"they":[23],"can":[24],"detect":[25],"anomalies":[26],"identify":[28],"failure":[29],"in":[30,75,125,192],"real":[31,77],"time.":[32],"However,":[33],"due":[34],"to":[35,67,117,138,198],"a":[36,81,155,160,165],"number":[38],"KPIs":[41,61],"the":[43,76,119,133],"lack":[44],"high-quality":[46,144],"labels,":[47],"existing":[48,178],"KPI":[49,72,82,126],"anomaly":[50,73,83,94,127],"detection":[51,74,84],"approaches":[52],"either":[53],"perform":[54],"well":[55],"only":[56],"on":[57,97,154],"certain":[58],"types":[59],"or":[62],"consume":[63],"excessive":[64],"resources.":[65],"Therefore,":[66],"realize":[68],"generic":[69],"practical":[71],"world,":[78],"we":[79,109,130],"propose":[80,111],"framework":[85],"named":[86],"iRRCF-Active,":[87],"which":[88],"contains":[89],"an":[90,104,112],"unsupervised":[91,182],"white-box":[93],"detector":[95],"based":[96],"Robust":[98],"Random":[99],"Cut":[100],"Forest":[101],"(RRCF),":[102],"active":[105,136],"learning":[106,137,183,187],"component.":[107],"Specifically,":[108],"novelly":[110],"improved":[113],"RRCF":[114,124],"(iRRCF)":[115],"algorithm":[116],"overcome":[118],"drawbacks":[120],"applying":[122],"original":[123],"detection.":[128],"Besides,":[129,189],"also":[131,195],"incorporate":[132],"idea":[134],"make":[139],"our":[140],"model":[141],"benefit":[142],"from":[143,164],"labels":[145],"given":[146],"by":[147],"experienced":[148],"operators.":[149],"We":[150],"conduct":[151],"extensive":[152],"experiments":[153],"large-scale":[156],"public":[157],"dataset":[158,162],"private":[161],"collected":[163],"commercial":[167],"bank.":[168],"The":[169],"experimental":[170],"resulta":[171],"demonstrate":[172],"iRRCF-Active":[174,193],"performs":[175],"better":[176],"than":[177],"traditional":[179],"statistical":[180],"methods,":[181],"methods":[184],"supervised":[186],"methods.":[188],"each":[190],"component":[191],"has":[194],"been":[196],"demonstrated":[197],"be":[199],"effective":[200],"indispensable.":[202]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":3}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
