{"id":"https://openalex.org/W3010065255","doi":"https://doi.org/10.1109/globecom38437.2019.9013302","title":"Time-Related Network Intrusion Detection Model: A Deep Learning Method","display_name":"Time-Related Network Intrusion Detection Model: A Deep Learning Method","publication_year":2019,"publication_date":"2019-12-01","ids":{"openalex":"https://openalex.org/W3010065255","doi":"https://doi.org/10.1109/globecom38437.2019.9013302","mag":"3010065255"},"language":"en","primary_location":{"id":"doi:10.1109/globecom38437.2019.9013302","is_oa":false,"landing_page_url":"https://doi.org/10.1109/globecom38437.2019.9013302","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE Global Communications Conference (GLOBECOM)","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/A5038594863","display_name":"Yun Lin","orcid":"https://orcid.org/0000-0003-1379-9301"},"institutions":[{"id":"https://openalex.org/I151727225","display_name":"Harbin Engineering University","ror":"https://ror.org/03x80pn82","country_code":"CN","type":"education","lineage":["https://openalex.org/I151727225"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yun Lin","raw_affiliation_strings":["College of Information and Communication Engineering, Harbin Engineering University, Harbin, P.R.China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Information and Communication Engineering, Harbin Engineering University, Harbin, P.R.China","institution_ids":["https://openalex.org/I151727225"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100440079","display_name":"Jie Wang","orcid":"https://orcid.org/0000-0002-3772-426X"},"institutions":[{"id":"https://openalex.org/I151727225","display_name":"Harbin Engineering University","ror":"https://ror.org/03x80pn82","country_code":"CN","type":"education","lineage":["https://openalex.org/I151727225"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jie Wang","raw_affiliation_strings":["College of Information and Communication Engineering, Harbin Engineering University, Harbin, P.R.China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Information and Communication Engineering, Harbin Engineering University, Harbin, P.R.China","institution_ids":["https://openalex.org/I151727225"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057166109","display_name":"Ya Tu","orcid":"https://orcid.org/0000-0001-5081-5329"},"institutions":[{"id":"https://openalex.org/I151727225","display_name":"Harbin Engineering University","ror":"https://ror.org/03x80pn82","country_code":"CN","type":"education","lineage":["https://openalex.org/I151727225"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ya Tu","raw_affiliation_strings":["College of Information and Communication Engineering, Harbin Engineering University, Harbin, P.R.China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Information and Communication Engineering, Harbin Engineering University, Harbin, P.R.China","institution_ids":["https://openalex.org/I151727225"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100333516","display_name":"Lei Chen","orcid":"https://orcid.org/0000-0002-8257-5806"},"institutions":[{"id":"https://openalex.org/I39815113","display_name":"Georgia Southern University","ror":"https://ror.org/04agmb972","country_code":"US","type":"education","lineage":["https://openalex.org/I39815113"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lei Chen","raw_affiliation_strings":["Department of Information Technology, College of Engineering and Computing, Georgia Southern University, Georgia, U.S.A"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Information Technology, College of Engineering and Computing, Georgia Southern University, Georgia, U.S.A","institution_ids":["https://openalex.org/I39815113"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101533510","display_name":"Zheng Dou","orcid":"https://orcid.org/0000-0001-8558-0689"},"institutions":[{"id":"https://openalex.org/I151727225","display_name":"Harbin Engineering University","ror":"https://ror.org/03x80pn82","country_code":"CN","type":"education","lineage":["https://openalex.org/I151727225"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zheng Dou","raw_affiliation_strings":["College of Information and Communication Engineering, Harbin Engineering University, Harbin, P.R.China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Information and Communication Engineering, Harbin Engineering University, Harbin, P.R.China","institution_ids":["https://openalex.org/I151727225"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":37,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"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":0.9988999962806702,"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":0.9972000122070312,"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":0.8563770055770874},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.8276557326316833},{"id":"https://openalex.org/keywords/intrusion-detection-system","display_name":"Intrusion detection system","score":0.7269309759140015},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7073002457618713},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.7004860043525696},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.6712232828140259},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6628882884979248},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6013590097427368},{"id":"https://openalex.org/keywords/constant-false-alarm-rate","display_name":"Constant false alarm rate","score":0.5410188436508179},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.46832066774368286},{"id":"https://openalex.org/keywords/binary-classification","display_name":"Binary classification","score":0.4370880126953125},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.42733463644981384}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8563770055770874},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.8276557326316833},{"id":"https://openalex.org/C35525427","wikidata":"https://www.wikidata.org/wiki/Q745881","display_name":"Intrusion detection system","level":2,"score":0.7269309759140015},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7073002457618713},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.7004860043525696},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.6712232828140259},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6628882884979248},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6013590097427368},{"id":"https://openalex.org/C77052588","wikidata":"https://www.wikidata.org/wiki/Q644307","display_name":"Constant false alarm rate","level":2,"score":0.5410188436508179},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.46832066774368286},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.4370880126953125},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.42733463644981384},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/globecom38437.2019.9013302","is_oa":false,"landing_page_url":"https://doi.org/10.1109/globecom38437.2019.9013302","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE Global Communications Conference (GLOBECOM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6100000143051147,"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1966809779","https://openalex.org/W2064675550","https://openalex.org/W2076063813","https://openalex.org/W2110798204","https://openalex.org/W2136922672","https://openalex.org/W2334853001","https://openalex.org/W2335999708","https://openalex.org/W2755146079","https://openalex.org/W2756489700","https://openalex.org/W2762776925","https://openalex.org/W2799308006","https://openalex.org/W2804368608","https://openalex.org/W2853755454","https://openalex.org/W2874136421","https://openalex.org/W2883554196","https://openalex.org/W2889010933","https://openalex.org/W2889109290","https://openalex.org/W2889165715","https://openalex.org/W2909665293","https://openalex.org/W2910482310","https://openalex.org/W2924353071","https://openalex.org/W3102259344","https://openalex.org/W6676481782","https://openalex.org/W6744671649"],"related_works":["https://openalex.org/W3013693939","https://openalex.org/W2159052453","https://openalex.org/W2566616303","https://openalex.org/W3131327266","https://openalex.org/W2734887215","https://openalex.org/W4297051394","https://openalex.org/W2752972570","https://openalex.org/W2145836866","https://openalex.org/W4309131238","https://openalex.org/W4380355000"],"abstract_inverted_index":{"Network":[0,33,90,175],"Intrusion":[1],"Detection":[2],"Systems":[3],"(NIDS)":[4],"have":[5],"become":[6],"a":[7,38,47,77,104],"strong":[8],"tool":[9],"to":[10,52,64,161],"alarm":[11,147],"attacks":[12],"in":[13,27],"computer":[14],"and":[15,31,144,169,176],"communication":[16],"systems.":[17],"Machine":[18],"learning,":[19,22],"especially":[20],"deep":[21,49],"has":[23],"made":[24],"huge":[25],"success":[26],"fields":[28],"of":[29,87,96,113,137,156,163],"industry":[30],"academic.":[32],"intrusion":[34,80,128],"activity":[35],"can":[36],"be":[37],"time":[39,115],"series":[40],"event.":[41],"In":[42],"this":[43],"paper,":[44],"we":[45,75],"adopt":[46],"time-related":[48],"learning":[50],"approach":[51,98,140,168],"detect":[53],"network":[54],"intrusions.":[55],"A":[56],"stacked":[57],"sparse":[58],"autoencoder":[59],"(SSAE)":[60],"is":[61,118,125,149,159],"first":[62],"built":[63],"extract":[65],"the":[66,69,85,94,100,111,135,138,145,164],"features":[67],"with":[68,103],"greedy":[70],"layer-wise":[71],"strategy.":[72],"And":[73],"then,":[74],"propose":[76],"time-":[78,122],"related":[79,123],"detection":[81],"system":[82],"based":[83,167,171],"on":[84,99,110,172],"variants":[86],"Recurrent":[88],"Neural":[89,174],"(RNN).":[91],"We":[92],"study":[93,112],"performance":[95,155],"proposed":[97,139],"binary":[101],"classification":[102],"benchmark":[105],"dataset":[106],"UNSW-":[107],"NB15.":[108],"Based":[109],"parameter":[114],"steps,":[116],"it":[117],"proved":[119],"that":[120,134,162],"our":[121,157],"model":[124,158],"effective":[126],"for":[127],"detection.":[129],"The":[130,154],"experiment":[131],"results":[132],"show":[133],"accuracy":[136],"reaches":[141],"over":[142],"98%":[143],"false":[146],"rate":[148],"as":[150,152],"low":[151],"1.8%.":[153],"superior":[160],"standard":[165],"RNN-":[166],"approaches":[170],"Deep":[173],"shallow":[177],"machine":[178],"learning.":[179]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":9},{"year":2021,"cited_by_count":9},{"year":2020,"cited_by_count":3}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}