{"id":"https://openalex.org/W4309679717","doi":"https://doi.org/10.1109/kse56063.2022.9953763","title":"A Novel Deep Clustering Variational Auto-Encoder for Anomaly-based Network Intrusion Detection","display_name":"A Novel Deep Clustering Variational Auto-Encoder for Anomaly-based Network Intrusion Detection","publication_year":2022,"publication_date":"2022-10-19","ids":{"openalex":"https://openalex.org/W4309679717","doi":"https://doi.org/10.1109/kse56063.2022.9953763"},"language":"en","primary_location":{"id":"doi:10.1109/kse56063.2022.9953763","is_oa":false,"landing_page_url":"https://doi.org/10.1109/kse56063.2022.9953763","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 14th International Conference on Knowledge and Systems Engineering (KSE)","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/A5100769135","display_name":"Van Qu\u00e2n Nguyen","orcid":"https://orcid.org/0000-0002-7654-5184"},"institutions":[{"id":"https://openalex.org/I131359167","display_name":"Le Quy Don Technical University","ror":"https://ror.org/04wgyjv21","country_code":"VN","type":"education","lineage":["https://openalex.org/I131359167"]}],"countries":["VN"],"is_corresponding":true,"raw_author_name":"Van Quan Nguyen","raw_affiliation_strings":["Le Quy Don Technical University,Viet Nam","Le Quy Don Technical University, Viet Nam"],"affiliations":[{"raw_affiliation_string":"Le Quy Don Technical University,Viet Nam","institution_ids":["https://openalex.org/I131359167"]},{"raw_affiliation_string":"Le Quy Don Technical University, Viet Nam","institution_ids":["https://openalex.org/I131359167"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100686339","display_name":"Viet Hung Nguyen","orcid":"https://orcid.org/0000-0002-9818-4455"},"institutions":[{"id":"https://openalex.org/I131359167","display_name":"Le Quy Don Technical University","ror":"https://ror.org/04wgyjv21","country_code":"VN","type":"education","lineage":["https://openalex.org/I131359167"]}],"countries":["VN"],"is_corresponding":false,"raw_author_name":"Viet Hung Nguyen","raw_affiliation_strings":["Le Quy Don Technical University,Viet Nam","Le Quy Don Technical University, Viet Nam"],"affiliations":[{"raw_affiliation_string":"Le Quy Don Technical University,Viet Nam","institution_ids":["https://openalex.org/I131359167"]},{"raw_affiliation_string":"Le Quy Don Technical University, Viet Nam","institution_ids":["https://openalex.org/I131359167"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068112879","display_name":"Tuan-Hao Hoang","orcid":"https://orcid.org/0000-0001-9742-2370"},"institutions":[{"id":"https://openalex.org/I131359167","display_name":"Le Quy Don Technical University","ror":"https://ror.org/04wgyjv21","country_code":"VN","type":"education","lineage":["https://openalex.org/I131359167"]}],"countries":["VN"],"is_corresponding":false,"raw_author_name":"Tuan Hao Hoang","raw_affiliation_strings":["Le Quy Don Technical University,Viet Nam","Le Quy Don Technical University, Viet Nam"],"affiliations":[{"raw_affiliation_string":"Le Quy Don Technical University,Viet Nam","institution_ids":["https://openalex.org/I131359167"]},{"raw_affiliation_string":"Le Quy Don Technical University, Viet Nam","institution_ids":["https://openalex.org/I131359167"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5069261349","display_name":"Nathan Shone","orcid":"https://orcid.org/0000-0002-7920-9434"},"institutions":[{"id":"https://openalex.org/I63098007","display_name":"Liverpool John Moores University","ror":"https://ror.org/04zfme737","country_code":"GB","type":"education","lineage":["https://openalex.org/I63098007"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Nathan Shone","raw_affiliation_strings":["Liverpool John Moores University,UK","Liverpool John Moores University, UK"],"affiliations":[{"raw_affiliation_string":"Liverpool John Moores University,UK","institution_ids":["https://openalex.org/I63098007"]},{"raw_affiliation_string":"Liverpool John Moores University, UK","institution_ids":["https://openalex.org/I63098007"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100769135"],"corresponding_institution_ids":["https://openalex.org/I131359167"],"apc_list":null,"apc_paid":null,"fwci":1.7947,"has_fulltext":false,"cited_by_count":13,"citation_normalized_percentile":{"value":0.85654459,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"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":0.9987999796867371,"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/T11598","display_name":"Internet Traffic Analysis and Secure E-voting","score":0.9930999875068665,"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/autoencoder","display_name":"Autoencoder","score":0.9308559894561768},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.753532350063324},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.7246761918067932},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.6671919226646423},{"id":"https://openalex.org/keywords/intrusion-detection-system","display_name":"Intrusion detection system","score":0.5787997245788574},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5623964071273804},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.5326518416404724},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5238147377967834},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4982283115386963},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4869924485683441},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.43607646226882935},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4289090633392334},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4280554950237274},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.4169292449951172},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.37425118684768677}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.9308559894561768},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.753532350063324},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7246761918067932},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.6671919226646423},{"id":"https://openalex.org/C35525427","wikidata":"https://www.wikidata.org/wiki/Q745881","display_name":"Intrusion detection system","level":2,"score":0.5787997245788574},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5623964071273804},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.5326518416404724},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5238147377967834},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4982283115386963},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4869924485683441},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.43607646226882935},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4289090633392334},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4280554950237274},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.4169292449951172},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.37425118684768677},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/kse56063.2022.9953763","is_oa":false,"landing_page_url":"https://doi.org/10.1109/kse56063.2022.9953763","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 14th International Conference on Knowledge and Systems Engineering (KSE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","score":0.49000000953674316,"display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W1909320841","https://openalex.org/W1959608418","https://openalex.org/W2009086942","https://openalex.org/W2077488147","https://openalex.org/W2099940443","https://openalex.org/W2106732331","https://openalex.org/W2150593711","https://openalex.org/W2296509296","https://openalex.org/W2789828921","https://openalex.org/W2807998075","https://openalex.org/W2910068345","https://openalex.org/W2965790822","https://openalex.org/W2969495950","https://openalex.org/W2981025625","https://openalex.org/W3040266635","https://openalex.org/W3087041179","https://openalex.org/W3093410479","https://openalex.org/W3109825968","https://openalex.org/W3132109934","https://openalex.org/W3135550350","https://openalex.org/W3136021864","https://openalex.org/W3153872861","https://openalex.org/W3203072121","https://openalex.org/W3205985117","https://openalex.org/W4205156564","https://openalex.org/W4211203352","https://openalex.org/W4224211479","https://openalex.org/W6639732818","https://openalex.org/W6640963894","https://openalex.org/W6758101687","https://openalex.org/W7016021835"],"related_works":["https://openalex.org/W3186512740","https://openalex.org/W3194885736","https://openalex.org/W4363671829","https://openalex.org/W2983142544","https://openalex.org/W2891059443","https://openalex.org/W4281663961","https://openalex.org/W3208888551","https://openalex.org/W4313561566","https://openalex.org/W3208386644","https://openalex.org/W4220682630"],"abstract_inverted_index":{"The":[0,176,207,235],"role":[1],"of":[2,37,52,59,85,101,157,169,181,193,209],"semi-supervised":[3,90,246],"network":[4,54,158,196,218],"intrusion":[5,159,219],"detection":[6],"systems":[7],"is":[8,213],"becoming":[9],"increasingly":[10],"important":[11],"in":[12,20,69,88],"the":[13,18,38,48,57,60,63,70,82,126,130,150,155,170,182,189,194,204,210,242],"ever-changing":[14],"digital":[15],"landscape.":[16],"Despite":[17],"boom":[19],"commercial":[21],"and":[22,56,65,98,106,153,166,224,233],"research":[23,74],"interest,":[24],"there":[25],"are":[26,45,111,184],"still":[27,112],"many":[28,77],"concerns":[29],"over":[30],"accuracy":[31],"yet":[32],"to":[33,42,80,119,128,148,173,187,202],"be":[34,174],"addressed.":[35],"Two":[36],"major":[39],"limitations":[40,152,205],"contributing":[41],"this":[43,135],"concern":[44],"reliably":[46],"learning":[47,146,179],"underlying":[49,190],"probability":[50,177,191],"distribution":[51,178,192],"normal":[53,64,86,195],"data":[55,67,87,132],"identification":[58],"boundary":[61],"between":[62],"anomalous":[66],"regions":[68],"latent":[71,83,171],"space.":[72],"Recent":[73],"has":[75],"proposed":[76,211,243],"different":[78],"ways":[79],"learn":[81,188],"representation":[84,168],"a":[89,139,163],"manner,":[91],"such":[92,109],"as":[93],"using":[94,215],"Clustering-based":[95],"Autoencoder":[96,143],"(CAE)":[97],"hybridized":[99],"approaches":[100,110,247],"Principal":[102],"Component":[103],"Analysis":[104],"(PCA)":[105],"CAE.":[107],"However,":[108],"affected":[113],"by":[114],"these":[115],"limitations,":[116],"predominantly":[117],"due":[118],"an":[120],"overreliance":[121],"on":[122],"feature":[123],"engineering,":[124],"or":[125],"inability":[127],"handle":[129],"large":[131],"dimensionality.":[133],"In":[134],"paper,":[136],"we":[137],"propose":[138],"novel":[140],"Cluster":[141],"Variational":[142],"(CVAE)":[144],"deep":[145],"model":[147,212],"overcome":[149],"aforementioned":[151],"increase":[154],"efficiency":[156],"detection.":[160],"This":[161,198],"enables":[162,200],"more":[164],"concise":[165],"dominant":[167],"space":[172],"learnt.":[175],"capabilities":[180],"VAE":[183],"fully":[185],"exploited":[186],"data.":[197],"combination":[199],"us":[201],"address":[203],"discussed.":[206],"performance":[208],"evaluated":[214],"eight":[216],"benchmark":[217],"datasets:":[220],"NSL-KDD,":[221],"UNSW-NB15,":[222],"CICIDS2017":[223],"five":[225],"scenarios":[226],"from":[227,248],"CTU13":[228],"(CTU13-08,":[229],"CTU-13-09,":[230],"CTU13-10,":[231],"CTU13-12":[232],"CTU13-13).":[234],"experimental":[236],"results":[237],"achieved":[238],"clearly":[239],"demonstrate":[240],"that":[241],"method":[244],"outperforms":[245],"existing":[249],"works.":[250]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":7}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}