{"id":"https://openalex.org/W3008250732","doi":"https://doi.org/10.1109/tc.2020.2973631","title":"A Neural Network-Based On-device Learning Anomaly Detector for Edge Devices","display_name":"A Neural Network-Based On-device Learning Anomaly Detector for Edge Devices","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W3008250732","doi":"https://doi.org/10.1109/tc.2020.2973631","mag":"3008250732"},"language":"en","primary_location":{"id":"doi:10.1109/tc.2020.2973631","is_oa":true,"landing_page_url":"https://doi.org/10.1109/tc.2020.2973631","pdf_url":"https://ieeexplore.ieee.org/ielx7/12/9112812/09000710.pdf","source":{"id":"https://openalex.org/S157670870","display_name":"IEEE Transactions on Computers","issn_l":"0018-9340","issn":["0018-9340","1557-9956","2326-3814"],"is_oa":false,"is_in_doaj":false,"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 Transactions on Computers","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://ieeexplore.ieee.org/ielx7/12/9112812/09000710.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5090218014","display_name":"Mineto Tsukada","orcid":"https://orcid.org/0000-0002-2426-4516"},"institutions":[{"id":"https://openalex.org/I203951103","display_name":"Keio University","ror":"https://ror.org/02kn6nx58","country_code":"JP","type":"education","lineage":["https://openalex.org/I203951103"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Mineto Tsukada","raw_affiliation_strings":["Keio University, Kohoku-ku, Japan"],"affiliations":[{"raw_affiliation_string":"Keio University, Kohoku-ku, Japan","institution_ids":["https://openalex.org/I203951103"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103017591","display_name":"Masaaki Kondo","orcid":"https://orcid.org/0000-0002-6025-8738"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Masaaki Kondo","raw_affiliation_strings":["The University of Tokyo, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"The University of Tokyo, Tokyo, Japan","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5041549339","display_name":"Hiroki Matsutani","orcid":"https://orcid.org/0000-0001-9578-3842"},"institutions":[{"id":"https://openalex.org/I203951103","display_name":"Keio University","ror":"https://ror.org/02kn6nx58","country_code":"JP","type":"education","lineage":["https://openalex.org/I203951103"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Hiroki Matsutani","raw_affiliation_strings":["Keio University, Kohoku-ku, Japan"],"affiliations":[{"raw_affiliation_string":"Keio University, Kohoku-ku, Japan","institution_ids":["https://openalex.org/I203951103"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5090218014"],"corresponding_institution_ids":["https://openalex.org/I203951103"],"apc_list":null,"apc_paid":null,"fwci":7.8845,"has_fulltext":true,"cited_by_count":83,"citation_normalized_percentile":{"value":0.97922055,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"1"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9998999834060669,"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":0.9998999834060669,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.9991999864578247,"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/computer-science","display_name":"Computer science","score":0.823383629322052},{"id":"https://openalex.org/keywords/backpropagation","display_name":"Backpropagation","score":0.5772263407707214},{"id":"https://openalex.org/keywords/concept-drift","display_name":"Concept drift","score":0.5685411691665649},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.5675811171531677},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.5332557559013367},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4740088880062103},{"id":"https://openalex.org/keywords/latency","display_name":"Latency (audio)","score":0.4600541293621063},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.35754474997520447},{"id":"https://openalex.org/keywords/embedded-system","display_name":"Embedded system","score":0.32862645387649536},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.30104267597198486},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.22517409920692444},{"id":"https://openalex.org/keywords/data-stream-mining","display_name":"Data stream mining","score":0.10577505826950073}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.823383629322052},{"id":"https://openalex.org/C155032097","wikidata":"https://www.wikidata.org/wiki/Q798503","display_name":"Backpropagation","level":3,"score":0.5772263407707214},{"id":"https://openalex.org/C60777511","wikidata":"https://www.wikidata.org/wiki/Q3045002","display_name":"Concept drift","level":3,"score":0.5685411691665649},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.5675811171531677},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.5332557559013367},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4740088880062103},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.4600541293621063},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.35754474997520447},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.32862645387649536},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.30104267597198486},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.22517409920692444},{"id":"https://openalex.org/C89198739","wikidata":"https://www.wikidata.org/wiki/Q3079880","display_name":"Data stream mining","level":2,"score":0.10577505826950073},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tc.2020.2973631","is_oa":true,"landing_page_url":"https://doi.org/10.1109/tc.2020.2973631","pdf_url":"https://ieeexplore.ieee.org/ielx7/12/9112812/09000710.pdf","source":{"id":"https://openalex.org/S157670870","display_name":"IEEE Transactions on Computers","issn_l":"0018-9340","issn":["0018-9340","1557-9956","2326-3814"],"is_oa":false,"is_in_doaj":false,"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 Transactions on Computers","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1109/tc.2020.2973631","is_oa":true,"landing_page_url":"https://doi.org/10.1109/tc.2020.2973631","pdf_url":"https://ieeexplore.ieee.org/ielx7/12/9112812/09000710.pdf","source":{"id":"https://openalex.org/S157670870","display_name":"IEEE Transactions on Computers","issn_l":"0018-9340","issn":["0018-9340","1557-9956","2326-3814"],"is_oa":false,"is_in_doaj":false,"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 Transactions on Computers","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Affordable and clean energy","score":0.9100000262260437,"id":"https://metadata.un.org/sdg/7"}],"awards":[{"id":"https://openalex.org/G3282004645","display_name":null,"funder_award_id":"JPMJCR","funder_id":"https://openalex.org/F4320338075","funder_display_name":"Core Research for Evolutional Science and Technology"}],"funders":[{"id":"https://openalex.org/F4320338075","display_name":"Core Research for Evolutional Science and Technology","ror":"https://ror.org/00097mb19"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3008250732.pdf","grobid_xml":"https://content.openalex.org/works/W3008250732.grobid-xml"},"referenced_works_count":76,"referenced_works":["https://openalex.org/W976938947","https://openalex.org/W1196375415","https://openalex.org/W1522301498","https://openalex.org/W1556024794","https://openalex.org/W1587559447","https://openalex.org/W1665214252","https://openalex.org/W1682403713","https://openalex.org/W1977838479","https://openalex.org/W1983278353","https://openalex.org/W2023639956","https://openalex.org/W2025768430","https://openalex.org/W2028499920","https://openalex.org/W2053724458","https://openalex.org/W2058128874","https://openalex.org/W2078841894","https://openalex.org/W2085989833","https://openalex.org/W2088900648","https://openalex.org/W2100495367","https://openalex.org/W2103496339","https://openalex.org/W2122301654","https://openalex.org/W2122646361","https://openalex.org/W2124756613","https://openalex.org/W2127979711","https://openalex.org/W2134603844","https://openalex.org/W2137385320","https://openalex.org/W2144182447","https://openalex.org/W2158054309","https://openalex.org/W2159637520","https://openalex.org/W2276069931","https://openalex.org/W2340896621","https://openalex.org/W2346720329","https://openalex.org/W2397052757","https://openalex.org/W2463279630","https://openalex.org/W2513568085","https://openalex.org/W2530417694","https://openalex.org/W2562773490","https://openalex.org/W2585560244","https://openalex.org/W2599354622","https://openalex.org/W2750384547","https://openalex.org/W2757876702","https://openalex.org/W2782009593","https://openalex.org/W2800445332","https://openalex.org/W2802251963","https://openalex.org/W2891935798","https://openalex.org/W2898485213","https://openalex.org/W2900362930","https://openalex.org/W2908324624","https://openalex.org/W2911599914","https://openalex.org/W2943592853","https://openalex.org/W2951911250","https://openalex.org/W2964121744","https://openalex.org/W2974071289","https://openalex.org/W2979359324","https://openalex.org/W2994502478","https://openalex.org/W3004277316","https://openalex.org/W3120740533","https://openalex.org/W3138798301","https://openalex.org/W3153872861","https://openalex.org/W4241536761","https://openalex.org/W4242577057","https://openalex.org/W4254182148","https://openalex.org/W4294620383","https://openalex.org/W4313169793","https://openalex.org/W6631190155","https://openalex.org/W6633310491","https://openalex.org/W6637242042","https://openalex.org/W6677828978","https://openalex.org/W6679935922","https://openalex.org/W6683199847","https://openalex.org/W6718594652","https://openalex.org/W6735108248","https://openalex.org/W6741548880","https://openalex.org/W6743688258","https://openalex.org/W6755052731","https://openalex.org/W6756794323","https://openalex.org/W6757581798"],"related_works":["https://openalex.org/W2894173309","https://openalex.org/W4206400463","https://openalex.org/W2157746493","https://openalex.org/W2792697259","https://openalex.org/W1495379181","https://openalex.org/W637433680","https://openalex.org/W2371065793","https://openalex.org/W2080531293","https://openalex.org/W2182604364","https://openalex.org/W3182876906"],"abstract_inverted_index":{"Semi-supervised":[0],"anomaly":[1,152],"detection":[2,153],"is":[3,103,171,196],"an":[4,156],"approach":[5],"to":[6,62,106,111],"identify":[7],"anomalies":[8],"by":[9],"learning":[10,110,124],"the":[11,50,55,66,168,175,180],"distribution":[12,67],"of":[13,28,68,163,184],"normal":[14,69],"data.":[15],"Backpropagation":[16],"neural":[17],"networks":[18],"(i.e.,":[19,71],"BP-NNs)":[20],"based":[21],"approaches":[22],"have":[23],"recently":[24],"drawn":[25],"attention":[26],"because":[27],"their":[29],"good":[30],"generalization":[31],"capability.":[32],"In":[33],"a":[34,191],"typical":[35],"situation,":[36],"BP-NN-based":[37],"models":[38],"are":[39,143],"iteratively":[40],"optimized":[41,105],"in":[42,65,115,155],"server":[43,81,142],"machines":[44],"with":[45],"input":[46],"data":[47,70,76,137],"gathered":[48],"from":[49],"edge":[51,79,126,140],"devices.":[52],"However,":[53],"(1)":[54],"iterative":[56],"optimization":[57],"often":[58],"requires":[59],"significant":[60],"efforts":[61],"follow":[63,112],"changes":[64],"concept":[72,113,160],"drift),":[73],"and":[74,80,85,95,141],"(2)":[75],"transfers":[77,138],"between":[78,139],"impose":[82],"additional":[83],"latency":[84,170],"energy":[86],"consumption.":[87],"To":[88],"address":[89],"these":[90],"issues,":[91],"we":[92],"propose":[93],"ONLAD":[94,100,102,120,149,164,185],"its":[96],"IP":[97],"core,":[98],"named":[99],"Core.":[101],"highly":[104],"perform":[107],"fast":[108],"sequential":[109],"drift":[114],"less":[116],"than":[117,174,199],"one":[118],"millisecond.":[119],"Core":[121,165,186],"realizes":[122,133],"on-device":[123],"for":[125],"devices":[127],"at":[128],"low":[129],"power":[130,182],"consumption,":[131],"which":[132],"standalone":[134],"execution":[135],"where":[136],"not":[144],"required.":[145],"Experiments":[146],"show":[147],"that":[148,158,167],"has":[150],"favorable":[151],"capability":[154],"environment":[157],"simulates":[159],"drift.":[161],"Evaluations":[162],"confirm":[166],"training":[169],"1.95x~6.58x":[172],"faster":[173],"other":[176],"software":[177],"implementations.":[178],"Also,":[179],"runtime":[181],"consumption":[183],"implemented":[187],"on":[188],"PYNQ-Z1":[189],"board,":[190],"small":[192],"FPGA/CPU":[193],"SoC":[194],"platform,":[195],"5.0x~25.4x":[197],"lower":[198],"them.":[200]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":10},{"year":2024,"cited_by_count":14},{"year":2023,"cited_by_count":21},{"year":2022,"cited_by_count":14},{"year":2021,"cited_by_count":16},{"year":2020,"cited_by_count":7}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}