{"id":"https://openalex.org/W3001100660","doi":"https://doi.org/10.1145/3336191.3371876","title":"Deep Learning for Anomaly Detection","display_name":"Deep Learning for Anomaly Detection","publication_year":2020,"publication_date":"2020-01-20","ids":{"openalex":"https://openalex.org/W3001100660","doi":"https://doi.org/10.1145/3336191.3371876","mag":"3001100660"},"language":"en","primary_location":{"id":"doi:10.1145/3336191.3371876","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3336191.3371876","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 13th International Conference on Web Search and Data Mining","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/A5015298086","display_name":"Ruoying Wang","orcid":"https://orcid.org/0009-0005-6338-4333"},"institutions":[{"id":"https://openalex.org/I1316064682","display_name":"LinkedIn (United States)","ror":"https://ror.org/02fyxhe35","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I1316064682"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Ruoying Wang","raw_affiliation_strings":["LinkedIn, Mountain View, CA, USA"],"affiliations":[{"raw_affiliation_string":"LinkedIn, Mountain View, CA, USA","institution_ids":["https://openalex.org/I1316064682"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019956606","display_name":"Kexin Nie","orcid":null},"institutions":[{"id":"https://openalex.org/I1316064682","display_name":"LinkedIn (United States)","ror":"https://ror.org/02fyxhe35","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I1316064682"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kexin Nie","raw_affiliation_strings":["LinkedIn, Mountain View, CA, USA"],"affiliations":[{"raw_affiliation_string":"LinkedIn, Mountain View, CA, USA","institution_ids":["https://openalex.org/I1316064682"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027610116","display_name":"Tie Wang","orcid":"https://orcid.org/0000-0002-8842-0813"},"institutions":[{"id":"https://openalex.org/I1316064682","display_name":"LinkedIn (United States)","ror":"https://ror.org/02fyxhe35","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I1316064682"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tie Wang","raw_affiliation_strings":["LinkedIn, Mountain View, CA, USA"],"affiliations":[{"raw_affiliation_string":"LinkedIn, Mountain View, CA, USA","institution_ids":["https://openalex.org/I1316064682"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100397623","display_name":"Yang Yang","orcid":"https://orcid.org/0000-0002-5245-3584"},"institutions":[{"id":"https://openalex.org/I1316064682","display_name":"LinkedIn (United States)","ror":"https://ror.org/02fyxhe35","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I1316064682"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yang Yang","raw_affiliation_strings":["LinkedIn, Mountain View, CA, USA"],"affiliations":[{"raw_affiliation_string":"LinkedIn, Mountain View, CA, USA","institution_ids":["https://openalex.org/I1316064682"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101920986","display_name":"Bo Long","orcid":"https://orcid.org/0000-0001-5129-8546"},"institutions":[{"id":"https://openalex.org/I1316064682","display_name":"LinkedIn (United States)","ror":"https://ror.org/02fyxhe35","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I1316064682"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Bo Long","raw_affiliation_strings":["LinkedIn, Mountain View, CA, USA"],"affiliations":[{"raw_affiliation_string":"LinkedIn, Mountain View, CA, USA","institution_ids":["https://openalex.org/I1316064682"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5015298086"],"corresponding_institution_ids":["https://openalex.org/I1316064682"],"apc_list":null,"apc_paid":null,"fwci":8.2316,"has_fulltext":false,"cited_by_count":90,"citation_normalized_percentile":{"value":0.98019578,"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":"894","last_page":"896"},"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":0.9988999962806702,"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":0.8743832111358643},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.7751299142837524},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7251831889152527},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.6400291919708252},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6131471991539001},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3724641799926758}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.8743832111358643},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.7751299142837524},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7251831889152527},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.6400291919708252},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6131471991539001},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3724641799926758},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"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":1,"locations":[{"id":"doi:10.1145/3336191.3371876","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3336191.3371876","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 13th International Conference on Web Search and Data Mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.44999998807907104,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W2108501770","https://openalex.org/W2125389028","https://openalex.org/W2398119937","https://openalex.org/W2460849547","https://openalex.org/W2469314752","https://openalex.org/W2474046725","https://openalex.org/W2516866958","https://openalex.org/W2528067192","https://openalex.org/W2592929672","https://openalex.org/W2599354622","https://openalex.org/W2609468941","https://openalex.org/W2741951152","https://openalex.org/W2743138268","https://openalex.org/W2768800090","https://openalex.org/W2785362611","https://openalex.org/W2786088545","https://openalex.org/W2787947370","https://openalex.org/W2790097716","https://openalex.org/W2793232926","https://openalex.org/W2803469628","https://openalex.org/W2886509629","https://openalex.org/W2910068345","https://openalex.org/W2911200746","https://openalex.org/W2951548452","https://openalex.org/W2954278343","https://openalex.org/W2963105487","https://openalex.org/W2963523189","https://openalex.org/W2963541464","https://openalex.org/W2964032056","https://openalex.org/W2964248614","https://openalex.org/W2968107576","https://openalex.org/W2975649990","https://openalex.org/W3013377280","https://openalex.org/W3098957257","https://openalex.org/W3153872861","https://openalex.org/W4300876526"],"related_works":["https://openalex.org/W2731899572","https://openalex.org/W2961085424","https://openalex.org/W2806741695","https://openalex.org/W3215138031","https://openalex.org/W4306674287","https://openalex.org/W3009238340","https://openalex.org/W4321369474","https://openalex.org/W4360585206","https://openalex.org/W4290647774","https://openalex.org/W3189286258"],"abstract_inverted_index":{"Anomaly":[0],"detection":[1,15,54,75,88,132],"has":[2,46],"been":[3],"widely":[4],"studied":[5],"and":[6,33,98,112],"used":[7,116],"in":[8,42,77,134],"diverse":[9],"applications.":[10],"Building":[11],"an":[12],"effective":[13],"anomaly":[14,31,53,74,87,131],"system":[16],"requires":[17],"the":[18,22,29,58,65,86,90,94,99,106,114,119],"researchers/developers":[19],"to":[20,50,57,117,125],"learn":[21],"complex":[23],"structure":[24],"from":[25,121,138],"noisy":[26],"data,":[27],"identify":[28],"dynamic":[30],"patterns":[32],"detect":[34],"anomalies":[35],"while":[36],"lacking":[37],"sufficient":[38],"labels.":[39],"Recent":[40],"advancement":[41],"deep":[43,72,95,108,129],"learning":[44,109],"techniques":[45,76,115,133],"made":[47],"it":[48,82,101,104,127],"possible":[49],"largely":[51],"improve":[52],"performance":[55],"compared":[56],"classical":[59],"approaches.":[60],"This":[61],"tutorial":[62,143],"will":[63],"help":[64],"audience":[66],"gain":[67],"a":[68,146],"comprehensive":[69],"understanding":[70],"of":[71,148],"learning-based":[73],"various":[78],"application":[79],"domains.":[80],"First,":[81],"introduces":[83],"what":[84],"is":[85],"problem,":[89],"approaches":[91],"taken":[92],"before":[93],"model":[96,130],"era":[97],"challenges":[100],"faced.":[102],"Then":[103],"surveys":[105],"state-of-the-art":[107],"models":[110],"extensively":[111],"discusses":[113],"overcome":[118],"limitations":[120],"traditional":[122],"algorithms.":[123],"Second":[124],"last,":[126],"studies":[128],"real":[135],"world":[136],"examples":[137],"LinkedIn":[139],"production":[140],"systems.":[141],"The":[142],"concludes":[144],"with":[145],"discussion":[147],"future":[149],"trends.":[150]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":17},{"year":2024,"cited_by_count":10},{"year":2023,"cited_by_count":19},{"year":2022,"cited_by_count":15},{"year":2021,"cited_by_count":18},{"year":2020,"cited_by_count":8}],"updated_date":"2026-03-27T14:29:43.386196","created_date":"2025-10-10T00:00:00"}