{"id":"https://openalex.org/W3100031421","doi":"https://doi.org/10.1109/access.2020.3037322","title":"An Integrated Model Based on O-GAN and Density Estimation for Anomaly Detection","display_name":"An Integrated Model Based on O-GAN and Density Estimation for Anomaly Detection","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W3100031421","doi":"https://doi.org/10.1109/access.2020.3037322","mag":"3100031421"},"language":"en","primary_location":{"id":"doi:10.1109/access.2020.3037322","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.3037322","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09256316.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09256316.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5075246417","display_name":"Shuo Liu","orcid":"https://orcid.org/0000-0002-1183-2238"},"institutions":[{"id":"https://openalex.org/I1456306","display_name":"North China University of Technology","ror":"https://ror.org/01nky7652","country_code":"CN","type":"education","lineage":["https://openalex.org/I1456306"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Shuo Liu","raw_affiliation_strings":["College of Science, North China University of Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"College of Science, North China University of Technology, Beijing, China","institution_ids":["https://openalex.org/I1456306"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5087197748","display_name":"Liwen Xu","orcid":"https://orcid.org/0000-0003-2133-8257"},"institutions":[{"id":"https://openalex.org/I1456306","display_name":"North China University of Technology","ror":"https://ror.org/01nky7652","country_code":"CN","type":"education","lineage":["https://openalex.org/I1456306"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liwen Xu","raw_affiliation_strings":["College of Science, North China University of Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"College of Science, North China University of Technology, Beijing, China","institution_ids":["https://openalex.org/I1456306"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5075246417"],"corresponding_institution_ids":["https://openalex.org/I1456306"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.2719,"has_fulltext":true,"cited_by_count":5,"citation_normalized_percentile":{"value":0.65583498,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"8","issue":null,"first_page":"204471","last_page":"204482"},"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":0.9861999750137329,"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/T12391","display_name":"Artificial Immune Systems Applications","score":0.9700999855995178,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.8113319873809814},{"id":"https://openalex.org/keywords/kernel-density-estimation","display_name":"Kernel density estimation","score":0.7126686573028564},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6929070949554443},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.6417374610900879},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.551811695098877},{"id":"https://openalex.org/keywords/probability-density-function","display_name":"Probability density function","score":0.5504785180091858},{"id":"https://openalex.org/keywords/density-estimation","display_name":"Density estimation","score":0.5475335121154785},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.5428095459938049},{"id":"https://openalex.org/keywords/variable-kernel-density-estimation","display_name":"Variable kernel density estimation","score":0.5298554301261902},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5086458921432495},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.49896907806396484},{"id":"https://openalex.org/keywords/multivariate-kernel-density-estimation","display_name":"Multivariate kernel density estimation","score":0.45236843824386597},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.41160228848457336},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4065515398979187},{"id":"https://openalex.org/keywords/kernel-method","display_name":"Kernel method","score":0.27519500255584717},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.21656793355941772},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.15408825874328613},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.11344760656356812}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.8113319873809814},{"id":"https://openalex.org/C71134354","wikidata":"https://www.wikidata.org/wiki/Q458825","display_name":"Kernel density estimation","level":3,"score":0.7126686573028564},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6929070949554443},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.6417374610900879},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.551811695098877},{"id":"https://openalex.org/C197055811","wikidata":"https://www.wikidata.org/wiki/Q207522","display_name":"Probability density function","level":2,"score":0.5504785180091858},{"id":"https://openalex.org/C189508267","wikidata":"https://www.wikidata.org/wiki/Q17088227","display_name":"Density estimation","level":3,"score":0.5475335121154785},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.5428095459938049},{"id":"https://openalex.org/C195699287","wikidata":"https://www.wikidata.org/wiki/Q7915722","display_name":"Variable kernel density estimation","level":4,"score":0.5298554301261902},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5086458921432495},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.49896907806396484},{"id":"https://openalex.org/C84894716","wikidata":"https://www.wikidata.org/wiki/Q6935135","display_name":"Multivariate kernel density estimation","level":5,"score":0.45236843824386597},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.41160228848457336},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4065515398979187},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.27519500255584717},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.21656793355941772},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.15408825874328613},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.11344760656356812},{"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},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2020.3037322","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.3037322","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09256316.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:ec6c1efb8a86429798da37a536353b7a","is_oa":true,"landing_page_url":"https://doaj.org/article/ec6c1efb8a86429798da37a536353b7a","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 8, Pp 204471-204482 (2020)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2020.3037322","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.3037322","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09256316.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3269172432","display_name":null,"funder_award_id":"20BTJ046","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4471220523","display_name":null,"funder_award_id":"20BTJ046","funder_id":"https://openalex.org/F4320335869","funder_display_name":"National Social Science Fund of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335869","display_name":"National Social Science Fund of China","ror":null}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3100031421.pdf","grobid_xml":"https://content.openalex.org/works/W3100031421.grobid-xml"},"referenced_works_count":79,"referenced_works":["https://openalex.org/W1546503963","https://openalex.org/W1663973292","https://openalex.org/W1851958107","https://openalex.org/W1986332411","https://openalex.org/W2026493302","https://openalex.org/W2064636555","https://openalex.org/W2099471712","https://openalex.org/W2100294832","https://openalex.org/W2101234009","https://openalex.org/W2105497548","https://openalex.org/W2110784166","https://openalex.org/W2115627867","https://openalex.org/W2121770664","https://openalex.org/W2129281431","https://openalex.org/W2138621090","https://openalex.org/W2141718589","https://openalex.org/W2144182447","https://openalex.org/W2173520492","https://openalex.org/W2296719434","https://openalex.org/W2412320034","https://openalex.org/W2516866958","https://openalex.org/W2524141621","https://openalex.org/W2536122452","https://openalex.org/W2579352881","https://openalex.org/W2593414223","https://openalex.org/W2599354622","https://openalex.org/W2739540493","https://openalex.org/W2750384547","https://openalex.org/W2753705016","https://openalex.org/W2787947370","https://openalex.org/W2803187616","https://openalex.org/W2887370532","https://openalex.org/W2891273344","https://openalex.org/W2892153332","https://openalex.org/W2896600774","https://openalex.org/W2900599876","https://openalex.org/W2902758299","https://openalex.org/W2910068345","https://openalex.org/W2911200746","https://openalex.org/W2914570111","https://openalex.org/W2919933730","https://openalex.org/W2950359091","https://openalex.org/W2951939904","https://openalex.org/W2963045681","https://openalex.org/W2963049059","https://openalex.org/W2963470893","https://openalex.org/W2963684088","https://openalex.org/W2963759070","https://openalex.org/W2964268978","https://openalex.org/W2966772556","https://openalex.org/W2975649990","https://openalex.org/W3153872861","https://openalex.org/W4212863985","https://openalex.org/W4254182148","https://openalex.org/W4289406342","https://openalex.org/W4294568686","https://openalex.org/W4300672471","https://openalex.org/W4320013936","https://openalex.org/W6638926814","https://openalex.org/W6666567213","https://openalex.org/W6675354045","https://openalex.org/W6685352114","https://openalex.org/W6715501732","https://openalex.org/W6727862155","https://openalex.org/W6732662172","https://openalex.org/W6734564793","https://openalex.org/W6735204497","https://openalex.org/W6748495906","https://openalex.org/W6751420435","https://openalex.org/W6751917112","https://openalex.org/W6754355446","https://openalex.org/W6755078323","https://openalex.org/W6756319230","https://openalex.org/W6758101687","https://openalex.org/W6760109739","https://openalex.org/W6764027567","https://openalex.org/W6779669310","https://openalex.org/W6816776434","https://openalex.org/W7016021835"],"related_works":["https://openalex.org/W4241010850","https://openalex.org/W3212687977","https://openalex.org/W2583877436","https://openalex.org/W2355371556","https://openalex.org/W3123419490","https://openalex.org/W1569550976","https://openalex.org/W2144201579","https://openalex.org/W2899726877","https://openalex.org/W1834385407","https://openalex.org/W2776263260"],"abstract_inverted_index":{"Anomaly":[0],"detection":[1,61,165],"is":[2],"a":[3,54,128,148,173],"classic":[4],"and":[5,57,73,101,178,190],"crucial":[6],"problem":[7],"in":[8],"the":[9,24,67,78,83,89,96,104,110,123,135,141,156],"field":[10],"of":[11,27,85,92,98,114,118,143,158],"artificial":[12],"intelligence,":[13],"which":[14],"aims":[15],"to":[16,38,87,108,154],"find":[17],"instances":[18],"that":[19,182],"deviate":[20],"so":[21,152],"much":[22],"from":[23,31],"main":[25],"distribution":[26],"data":[28],"or":[29],"differ":[30],"known":[32],"instances.":[33],"This":[34],"paper":[35],"explores":[36],"how":[37],"combine":[39],"advanced":[40],"deep":[41],"learning":[42],"techniques":[43],"with":[44,162],"traditional":[45],"probabilistic":[46],"statistical":[47],"methods":[48,166],"for":[49],"anomaly":[50,60,159,164],"detection.":[51,160],"We":[52],"propose":[53],"very":[55,174],"effective":[56],"concise":[58],"semi-supervised":[59],"method":[62,171],"named":[63],"\u201cORGAN-KDE\u201d":[64],"based":[65,167],"on":[66,168,186],"orthogonal":[68],"generative":[69],"adversarial":[70],"network":[71,176],"(O-GAN)":[72],"kernel":[74,105,137],"density":[75,106,112,138],"estimation.":[76],"In":[77],"training":[79],"phase,":[80],"we":[81],"use":[82,103],"encoder":[84,125],"O-GAN":[86],"learn":[88],"latent":[90],"representation":[91],"normal":[93,99,119],"data,":[94,100],"namely":[95],"code":[97,117,142],"then":[102],"estimation":[107],"solve":[109],"probability":[111,130,150],"function":[113],"code.":[115],"The":[116],"sample":[120,145],"obtained":[121],"through":[122,134],"trained":[124,136],"can":[126,146],"get":[127,147],"larger":[129],"value":[131],"when":[132],"passing":[133],"estimator,":[139],"while":[140],"anomalous":[144],"smaller":[149],"value,":[151],"as":[153],"achieve":[155],"purpose":[157],"Compared":[161],"other":[163],"GAN,":[169],"our":[170],"has":[172],"simple":[175],"structure,":[177],"experiments":[179],"have":[180],"proved":[181],"it":[183],"performs":[184],"well":[185],"both":[187],"structured":[188],"datasets":[189],"image":[191],"datasets.":[192]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":2}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}