{"id":"https://openalex.org/W3012553816","doi":"https://doi.org/10.1109/aiccsa47632.2019.9035217","title":"A Lightweight Deep Autoencoder-Based Approach for Unsupervised Anomaly Detection","display_name":"A Lightweight Deep Autoencoder-Based Approach for Unsupervised Anomaly Detection","publication_year":2019,"publication_date":"2019-11-01","ids":{"openalex":"https://openalex.org/W3012553816","doi":"https://doi.org/10.1109/aiccsa47632.2019.9035217","mag":"3012553816"},"language":"en","primary_location":{"id":"doi:10.1109/aiccsa47632.2019.9035217","is_oa":false,"landing_page_url":"https://doi.org/10.1109/aiccsa47632.2019.9035217","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE/ACS 16th International Conference on Computer Systems and Applications (AICCSA)","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/A5033040150","display_name":"Gcinizwe Dlamini","orcid":"https://orcid.org/0000-0002-4578-5011"},"institutions":[{"id":"https://openalex.org/I4210116741","display_name":"Innopolis University","ror":"https://ror.org/02b7jh107","country_code":"RU","type":"education","lineage":["https://openalex.org/I4210116741"]}],"countries":["RU"],"is_corresponding":false,"raw_author_name":"Gcinizwe Dlamini","raw_affiliation_strings":["Innopolis University, Innopolis, Russia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Innopolis University, Innopolis, Russia","institution_ids":["https://openalex.org/I4210116741"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077893431","display_name":"Rufina Galieva","orcid":null},"institutions":[{"id":"https://openalex.org/I4210116741","display_name":"Innopolis University","ror":"https://ror.org/02b7jh107","country_code":"RU","type":"education","lineage":["https://openalex.org/I4210116741"]}],"countries":["RU"],"is_corresponding":false,"raw_author_name":"Rufina Galieva","raw_affiliation_strings":["Innopolis University, Innopolis, Russia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Innopolis University, Innopolis, Russia","institution_ids":["https://openalex.org/I4210116741"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5073275664","display_name":"Muhammad Fahim","orcid":"https://orcid.org/0000-0001-6259-5458"},"institutions":[{"id":"https://openalex.org/I4210116741","display_name":"Innopolis University","ror":"https://ror.org/02b7jh107","country_code":"RU","type":"education","lineage":["https://openalex.org/I4210116741"]}],"countries":["RU"],"is_corresponding":false,"raw_author_name":"Muhammad Fahim","raw_affiliation_strings":["Innopolis University, Innopolis, Russia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Innopolis University, Innopolis, Russia","institution_ids":["https://openalex.org/I4210116741"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210116741"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"1","issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10400","display_name":"Network Security and Intrusion Detection","score":1.0,"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"}},"topics":[{"id":"https://openalex.org/T10400","display_name":"Network Security and Intrusion Detection","score":1.0,"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/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/T11241","display_name":"Advanced Malware Detection Techniques","score":0.9986000061035156,"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/autoencoder","display_name":"Autoencoder","score":0.9722583889961243},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.8270156979560852},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.757147490978241},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6523430347442627},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.6194825172424316},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6105988025665283},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.5840677618980408},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.5345138311386108},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5144734382629395},{"id":"https://openalex.org/keywords/intrusion-detection-system","display_name":"Intrusion detection system","score":0.47353264689445496},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3941633701324463},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3780425786972046}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.9722583889961243},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.8270156979560852},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.757147490978241},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6523430347442627},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.6194825172424316},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6105988025665283},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.5840677618980408},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.5345138311386108},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5144734382629395},{"id":"https://openalex.org/C35525427","wikidata":"https://www.wikidata.org/wiki/Q745881","display_name":"Intrusion detection system","level":2,"score":0.47353264689445496},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3941633701324463},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3780425786972046},{"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}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/aiccsa47632.2019.9035217","is_oa":false,"landing_page_url":"https://doi.org/10.1109/aiccsa47632.2019.9035217","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE/ACS 16th International Conference on Computer Systems and Applications (AICCSA)","raw_type":"proceedings-article"},{"id":"mag:3042719998","is_oa":false,"landing_page_url":"https://jglobal.jst.go.jp/en/detail?JGLOBAL_ID=202002279433999200","pdf_url":null,"source":{"id":"https://openalex.org/S4306512817","display_name":"IEEE Conference Proceedings","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":null,"is_accepted":false,"is_published":null,"raw_source_name":"IEEE Conference Proceedings","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6899999976158142,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W103650626","https://openalex.org/W1544101352","https://openalex.org/W1969082358","https://openalex.org/W2057074436","https://openalex.org/W2069353545","https://openalex.org/W2075698219","https://openalex.org/W2122646361","https://openalex.org/W2151570219","https://openalex.org/W2169041657","https://openalex.org/W2399941526","https://openalex.org/W2606068338","https://openalex.org/W2786088545","https://openalex.org/W2807621303","https://openalex.org/W2921871306","https://openalex.org/W2950204666","https://openalex.org/W4229687706","https://openalex.org/W4241780302","https://openalex.org/W4245460974","https://openalex.org/W6763631378"],"related_works":["https://openalex.org/W3013693939","https://openalex.org/W2159052453","https://openalex.org/W3186512740","https://openalex.org/W3194885736","https://openalex.org/W4363671829","https://openalex.org/W4285233543","https://openalex.org/W2806873178","https://openalex.org/W2770818364","https://openalex.org/W2965146396","https://openalex.org/W4230838436"],"abstract_inverted_index":{"Unsupervised":[0],"anomaly":[1,82],"detection":[2,83,147],"is":[3,28,51,126],"an":[4,54],"important":[5],"area":[6],"of":[7,16,71,98,103,140,158,166],"research":[8],"to":[9,30,40,62,87,128,180],"find":[10],"abnormal":[11],"behavior":[12],"and":[13,48,105,161,163,169],"integral":[14],"part":[15],"many":[17],"systems.":[18,148],"In":[19,74],"this":[20,75],"research,":[21],"a":[22,175],"lightweight":[23,79],"deep":[24],"autoencoder":[25,80,109],"based":[26],"approach":[27],"presented":[29],"detect":[31,129],"anomalies":[32],"in":[33,85,96],"unsupervised":[34],"manner.":[35],"It":[36,172],"has":[37],"the":[38,42,45,60,68,72,137],"ability":[39],"learn":[41],"model":[43,152],"over":[44,136],"normal":[46,120],"patterns":[47,66],"any":[49],"deviation":[50],"considered":[52,118],"as":[53,145],"anomaly.":[55],"Consequently,":[56],"it":[57],"can":[58,92],"relax":[59],"condition":[61],"have":[63],"anomalous":[64],"data":[65,116],"during":[67],"training":[69],"phase":[70],"model.":[73],"work,":[76],"we":[77],"examine":[78],"for":[81,110],"task":[84],"order":[86],"show":[88,93],"that":[89],"simple":[90],"architecture":[91],"good":[94],"performance":[95],"terms":[97],"training,":[99],"testing":[100],"time,":[101],"number":[102],"parameters":[104],"metrics.":[106],"We":[107,149],"apply":[108],"binary":[111],"classification":[112],"problem":[113],"(i.e.,":[114],"each":[115],"point":[117],"either":[119],"or":[121],"abnormal).":[122],"The":[123,131],"reconstruction":[124],"error":[125],"used":[127],"anomalies.":[130],"experiments":[132],"are":[133],"carried":[134],"out":[135],"particular":[138],"class":[139],"cyber":[141],"security":[142],"domain":[143],"known":[144],"intrusion":[146],"evaluated":[150],"our":[151],"on":[153],"standard":[154],"publicly":[155],"available":[156],"benchmarks":[157],"KDD-99,":[159],"NSL-KDD":[160],"UNSW-NB15":[162],"achieved":[164],"F1-score":[165],"0.96,":[167],"0.88":[168],"0.95,":[170],"respectively.":[171],"outperforms":[173],"by":[174],"considerable":[176],"margin":[177],"when":[178],"compared":[179],"state-of-the-art":[181],"methods.":[182]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}