{"id":"https://openalex.org/W4282813397","doi":"https://doi.org/10.1145/3534678.3539348","title":"Adaptive Model Pooling for Online Deep Anomaly Detection from a Complex Evolving Data Stream","display_name":"Adaptive Model Pooling for Online Deep Anomaly Detection from a Complex Evolving Data Stream","publication_year":2022,"publication_date":"2022-08-12","ids":{"openalex":"https://openalex.org/W4282813397","doi":"https://doi.org/10.1145/3534678.3539348"},"language":"en","primary_location":{"id":"doi:10.1145/3534678.3539348","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3534678.3539348","pdf_url":null,"source":{"id":"https://openalex.org/S4363608767","display_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2206.04792","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5083900503","display_name":"Susik Yoon","orcid":"https://orcid.org/0000-0001-5596-4972"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Susik Yoon","raw_affiliation_strings":["UIUC, Champaign, IL, USA"],"affiliations":[{"raw_affiliation_string":"UIUC, Champaign, IL, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100738829","display_name":"Youngjun Lee","orcid":"https://orcid.org/0000-0002-6705-7883"},"institutions":[{"id":"https://openalex.org/I157485424","display_name":"Korea Advanced Institute of Science and Technology","ror":"https://ror.org/05apxxy63","country_code":"KR","type":"education","lineage":["https://openalex.org/I157485424"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Youngjun Lee","raw_affiliation_strings":["KAIST, Daejeon, South Korea"],"affiliations":[{"raw_affiliation_string":"KAIST, Daejeon, South Korea","institution_ids":["https://openalex.org/I157485424"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112008082","display_name":"Jae-Gil Lee","orcid":null},"institutions":[{"id":"https://openalex.org/I157485424","display_name":"Korea Advanced Institute of Science and Technology","ror":"https://ror.org/05apxxy63","country_code":"KR","type":"education","lineage":["https://openalex.org/I157485424"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jae-Gil Lee","raw_affiliation_strings":["KAIST, Daejeon, South Korea"],"affiliations":[{"raw_affiliation_string":"KAIST, Daejeon, South Korea","institution_ids":["https://openalex.org/I157485424"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5038390659","display_name":"Byung Suk Lee","orcid":"https://orcid.org/0000-0002-6019-5247"},"institutions":[{"id":"https://openalex.org/I111236770","display_name":"University of Vermont","ror":"https://ror.org/0155zta11","country_code":"US","type":"education","lineage":["https://openalex.org/I111236770"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Byung Suk Lee","raw_affiliation_strings":["University of Vermont, Burlington, VT, USA"],"affiliations":[{"raw_affiliation_string":"University of Vermont, Burlington, VT, USA","institution_ids":["https://openalex.org/I111236770"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5083900503"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.6648,"has_fulltext":false,"cited_by_count":37,"citation_normalized_percentile":{"value":0.94360524,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"2347","last_page":"2357"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12761","display_name":"Data Stream Mining Techniques","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/T12761","display_name":"Data Stream Mining Techniques","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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9997000098228455,"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":0.9994000196456909,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.85251784324646},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7986618280410767},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.7718737125396729},{"id":"https://openalex.org/keywords/data-stream-mining","display_name":"Data stream mining","score":0.6570636630058289},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.6109529733657837},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.5639654994010925},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.5309357643127441},{"id":"https://openalex.org/keywords/data-stream","display_name":"Data stream","score":0.4643775522708893},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4487919211387634},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.44402509927749634},{"id":"https://openalex.org/keywords/concept-drift","display_name":"Concept drift","score":0.4380655288696289},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.42771556973457336},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.411857545375824},{"id":"https://openalex.org/keywords/streaming-data","display_name":"Streaming data","score":0.41064342856407166},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.07862585783004761}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.85251784324646},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7986618280410767},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.7718737125396729},{"id":"https://openalex.org/C89198739","wikidata":"https://www.wikidata.org/wiki/Q3079880","display_name":"Data stream mining","level":2,"score":0.6570636630058289},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.6109529733657837},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.5639654994010925},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.5309357643127441},{"id":"https://openalex.org/C2778484313","wikidata":"https://www.wikidata.org/wiki/Q1172540","display_name":"Data stream","level":2,"score":0.4643775522708893},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4487919211387634},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.44402509927749634},{"id":"https://openalex.org/C60777511","wikidata":"https://www.wikidata.org/wiki/Q3045002","display_name":"Concept drift","level":3,"score":0.4380655288696289},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.42771556973457336},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.411857545375824},{"id":"https://openalex.org/C2777611316","wikidata":"https://www.wikidata.org/wiki/Q39045282","display_name":"Streaming data","level":2,"score":0.41064342856407166},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.07862585783004761},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","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}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3534678.3539348","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3534678.3539348","pdf_url":null,"source":{"id":"https://openalex.org/S4363608767","display_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2206.04792","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2206.04792","pdf_url":"https://arxiv.org/pdf/2206.04792","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2206.04792","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2206.04792","pdf_url":"https://arxiv.org/pdf/2206.04792","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[{"score":0.6499999761581421,"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":46,"referenced_works":["https://openalex.org/W1552339598","https://openalex.org/W1638081485","https://openalex.org/W2022851810","https://openalex.org/W2073256825","https://openalex.org/W2097178527","https://openalex.org/W2103012681","https://openalex.org/W2112796928","https://openalex.org/W2143991132","https://openalex.org/W2282861635","https://openalex.org/W2296719434","https://openalex.org/W2398119937","https://openalex.org/W2472119793","https://openalex.org/W2500517591","https://openalex.org/W2541884796","https://openalex.org/W2564960719","https://openalex.org/W2585508806","https://openalex.org/W2621614835","https://openalex.org/W2743138268","https://openalex.org/W2750384547","https://openalex.org/W2786088545","https://openalex.org/W2790057819","https://openalex.org/W2807955733","https://openalex.org/W2809400334","https://openalex.org/W2933168239","https://openalex.org/W2942810103","https://openalex.org/W2949071326","https://openalex.org/W2955213239","https://openalex.org/W2963739929","https://openalex.org/W2970207504","https://openalex.org/W2995944953","https://openalex.org/W3021596612","https://openalex.org/W3040266635","https://openalex.org/W3080585419","https://openalex.org/W3093580248","https://openalex.org/W3102015031","https://openalex.org/W3104788453","https://openalex.org/W3153183116","https://openalex.org/W3173351289","https://openalex.org/W4235688125","https://openalex.org/W4288335160","https://openalex.org/W4297814361","https://openalex.org/W4318619660","https://openalex.org/W6763324549","https://openalex.org/W6765541894","https://openalex.org/W6785333560","https://openalex.org/W6948268659"],"related_works":["https://openalex.org/W4281572076","https://openalex.org/W4307392573","https://openalex.org/W2802243998","https://openalex.org/W2469699777","https://openalex.org/W2736127210","https://openalex.org/W4389449520","https://openalex.org/W2060628068","https://openalex.org/W3208495060","https://openalex.org/W2277307313","https://openalex.org/W2235038291"],"abstract_inverted_index":{"Online":[0],"anomaly":[1,45,58,75,86,158,175],"detection":[2,46,59,87,159,176],"from":[3,29,62],"a":[4,70,119],"data":[5,27,65,95,140,147],"stream":[6],"is":[7,18],"critical":[8],"for":[9,41,72,125],"the":[10,49,53,63,91,114,126,129,132,138,157,162,172],"safety":[11],"and":[12,25,32,93,109,128,153,169,182],"security":[13],"of":[14,51,121,161,165,171],"many":[15],"applications":[16],"but":[17],"facing":[19],"severe":[20],"challenges":[21],"due":[22],"to":[23,136,180],"complex":[24,92],"evolving":[26,64,94,139],"streams":[28,96],"IoT":[30],"devices":[31],"cloud-based":[33],"infrastructures.":[34],"Unfortunately,":[35],"existing":[36],"approaches":[37],"fall":[38],"too":[39],"short":[40],"these":[42],"challenges;":[43],"online":[44,73],"methods":[47,60,168,177],"bear":[48],"burden":[50],"handling":[52],"complexity":[54],"while":[55],"offline":[56],"deep":[57,74,85],"suffer":[61],"distribution.":[66],"This":[67],"paper":[68],"presents":[69],"framework":[71],"detection,":[76],"ARCUS,":[77],"which":[78,149],"can":[79],"be":[80],"instantiated":[81],"with":[82,103,118,145],"any":[83],"autoencoder-based":[84,167],"methods.":[88],"It":[89],"handles":[90],"using":[97],"an":[98],"adaptive":[99],"model":[100,111,133],"pooling":[101],"approach":[102],"two":[104],"novel":[105],"techniques:":[106],"concept-driven":[107],"inference":[108],"drift-aware":[110],"pool":[112,134],"update;":[113],"former":[115],"detects":[116],"anomalies":[117],"combination":[120],"models":[122],"most":[123],"appropriate":[124],"complexity,":[127],"latter":[130],"adapts":[131],"dynamically":[135],"fit":[137],"streams.":[141],"In":[142],"comprehensive":[143],"experiments":[144],"ten":[146],"sets":[148],"are":[150],"both":[151],"high-dimensional":[152],"concept-drifted,":[154],"ARCUS":[155],"improved":[156],"accuracy":[160],"streaming":[163,174],"variants":[164],"state-of-the-art":[166,173],"that":[170],"by":[178],"up":[179],"22%":[181],"37%,":[183],"respectively.":[184]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":14},{"year":2024,"cited_by_count":12},{"year":2023,"cited_by_count":9}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}
