{"id":"https://openalex.org/W4390354591","doi":"https://doi.org/10.32604/cmc.2023.046607","title":"A Time Series Intrusion Detection Method Based on SSAE, TCN and Bi-LSTM","display_name":"A Time Series Intrusion Detection Method Based on SSAE, TCN and Bi-LSTM","publication_year":2023,"publication_date":"2023-12-28","ids":{"openalex":"https://openalex.org/W4390354591","doi":"https://doi.org/10.32604/cmc.2023.046607"},"language":"en","primary_location":{"id":"doi:10.32604/cmc.2023.046607","doi":"https://doi.org/10.32604/cmc.2023.046607","is_oa":true,"landing_page_url":"https://doi.org/10.32604/cmc.2023.046607","pdf_url":"https://www.techscience.com/cmc/online/detail/19656/pdf","source":{"id":"https://openalex.org/S4210191605","display_name":"Computers, materials & continua/Computers, materials & continua (Print)","issn_l":"1546-2218","issn":["1546-2218","1546-2226"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","dongbi-d","jufo-1","ki-jl-1","norway-1","poland-20","russia-white-list-1"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers, Materials & Continua","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://www.techscience.com/cmc/online/detail/19656/pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5090454892","display_name":"Zhenxiang He","orcid":"https://orcid.org/0009-0004-8441-9534","observed_orcids":["https://orcid.org/0009-0004-8441-9534","https://orcid.org/0009-0007-6458-6820"]},"institutions":[{"id":"https://openalex.org/I4210125346","display_name":"Gansu Institute of Political Science and Law","ror":"https://ror.org/025km4h87","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210125346"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhenxiang He","raw_affiliation_strings":["School of Cyberspace Security, Gansu University of Political Science and Law, Lanzhou, 730000, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Cyberspace Security, Gansu University of Political Science and Law, Lanzhou, 730000, China","institution_ids":["https://openalex.org/I4210125346"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5104221432","display_name":"Xunxi Wang","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4210125346","display_name":"Gansu Institute of Political Science and Law","ror":"https://ror.org/025km4h87","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210125346"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xunxi Wang","raw_affiliation_strings":["School of Cyberspace Security, Gansu University of Political Science and Law, Lanzhou, 730000, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Cyberspace Security, Gansu University of Political Science and Law, Lanzhou, 730000, China","institution_ids":["https://openalex.org/I4210125346"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100766916","display_name":"Chun-Wei Li","orcid":"https://orcid.org/0000-0003-4602-1504","observed_orcids":["https://orcid.org/0000-0003-4602-1504"]},"institutions":[{"id":"https://openalex.org/I4210125346","display_name":"Gansu Institute of Political Science and Law","ror":"https://ror.org/025km4h87","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210125346"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chunwei Li","raw_affiliation_strings":["School of Cyberspace Security, Gansu University of Political Science and Law, Lanzhou, 730000, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Cyberspace Security, Gansu University of Political Science and Law, Lanzhou, 730000, China","institution_ids":["https://openalex.org/I4210125346"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210125346"],"apc_list":null,"apc_paid":null,"fwci":1.0878,"has_fulltext":true,"cited_by_count":12,"citation_normalized_percentile":{"value":0.75514356,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":99},"biblio":{"volume":"78","issue":"1","first_page":"845","last_page":"871"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10400","display_name":"Network Security and Intrusion Detection","score":0.9984999895095825,"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":0.9984999895095825,"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/T12326","display_name":"Network Packet Processing and Optimization","score":0.0005000000237487257,"subfield":{"id":"https://openalex.org/subfields/1708","display_name":"Hardware and Architecture"},"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":0.00039999998989515007,"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/network-intrusion-detection","display_name":"network intrusion detection","score":0.9589999914169312},{"id":"https://openalex.org/keywords/time-series-analysis","display_name":"time series analysis","score":0.7170000076293945},{"id":"https://openalex.org/keywords/stacked-sparse-autoencoder","display_name":"stacked sparse autoencoder","score":0.9660000205039978},{"id":"https://openalex.org/keywords/temporal-convolutional-network","display_name":"temporal convolutional network","score":0.9890000224113464},{"id":"https://openalex.org/keywords/bidirectional-lstm","display_name":"bidirectional LSTM","score":0.9980000257492065},{"id":"https://openalex.org/keywords/network-traffic","display_name":"network traffic","score":0.5789999961853027},{"id":"https://openalex.org/keywords/temporal-feature-extraction","display_name":"temporal feature extraction","score":0.7400000095367432},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"dimensionality reduction","score":0.949999988079071},{"id":"https://openalex.org/keywords/unsw-nb15","display_name":"UNSW-NB15","score":0.7670000195503235},{"id":"https://openalex.org/keywords/cicids2017","display_name":"CICIDS2017","score":0.9120000004768372},{"id":"https://openalex.org/keywords/cybersecurity","display_name":"cybersecurity","score":0.3190000057220459}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8024016618728638},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6737076044082642},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6428061127662659},{"id":"https://openalex.org/C35525427","wikidata":"https://www.wikidata.org/wiki/Q745881","display_name":"Intrusion detection system","level":2,"score":0.559697151184082},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.5546414852142334},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5381138920783997},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4794125258922577},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4756949841976166},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.44700127840042114},{"id":"https://openalex.org/C177606310","wikidata":"https://www.wikidata.org/wiki/Q5674297","display_name":"Adaptability","level":2,"score":0.4351939260959625},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4327433705329895},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39242780208587646},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"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/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","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:10.32604/cmc.2023.046607","doi":"https://doi.org/10.32604/cmc.2023.046607","is_oa":true,"landing_page_url":"https://doi.org/10.32604/cmc.2023.046607","pdf_url":"https://www.techscience.com/cmc/online/detail/19656/pdf","source":{"id":"https://openalex.org/S4210191605","display_name":"Computers, materials & continua/Computers, materials & continua (Print)","issn_l":"1546-2218","issn":["1546-2218","1546-2226"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","dongbi-d","jufo-1","ki-jl-1","norway-1","poland-20","russia-white-list-1"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers, Materials & Continua","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.32604/cmc.2023.046607","doi":"https://doi.org/10.32604/cmc.2023.046607","is_oa":true,"landing_page_url":"https://doi.org/10.32604/cmc.2023.046607","pdf_url":"https://www.techscience.com/cmc/online/detail/19656/pdf","source":{"id":"https://openalex.org/S4210191605","display_name":"Computers, materials & continua/Computers, materials & continua (Print)","issn_l":"1546-2218","issn":["1546-2218","1546-2226"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","dongbi-d","jufo-1","ki-jl-1","norway-1","poland-20","russia-white-list-1"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computers, Materials & Continua","raw_type":"journal-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4390354591.pdf","grobid_xml":"https://content.openalex.org/works/W4390354591.grobid-xml"},"referenced_works_count":56,"referenced_works":["https://openalex.org/W46659105","https://openalex.org/W1689711448","https://openalex.org/W2095705004","https://openalex.org/W2338266296","https://openalex.org/W2399839733","https://openalex.org/W2743678626","https://openalex.org/W2789828921","https://openalex.org/W2792764867","https://openalex.org/W2795175906","https://openalex.org/W2809932464","https://openalex.org/W2941215571","https://openalex.org/W2950911565","https://openalex.org/W2958285686","https://openalex.org/W2958333154","https://openalex.org/W2964090065","https://openalex.org/W2979046245","https://openalex.org/W2980139481","https://openalex.org/W3010957669","https://openalex.org/W3042933481","https://openalex.org/W3082065751","https://openalex.org/W3113693716","https://openalex.org/W3118220620","https://openalex.org/W3135689386","https://openalex.org/W3157280532","https://openalex.org/W3164321464","https://openalex.org/W3165382753","https://openalex.org/W3167957704","https://openalex.org/W3168057860","https://openalex.org/W3168771234","https://openalex.org/W3189548043","https://openalex.org/W3195193442","https://openalex.org/W3197851632","https://openalex.org/W3200688867","https://openalex.org/W3214029542","https://openalex.org/W3214373139","https://openalex.org/W4205243841","https://openalex.org/W4205839677","https://openalex.org/W4206222891","https://openalex.org/W4220776964","https://openalex.org/W4223531348","https://openalex.org/W4233261744","https://openalex.org/W4283212112","https://openalex.org/W4293722527","https://openalex.org/W4298146241","https://openalex.org/W4300167448","https://openalex.org/W4307955810","https://openalex.org/W4312401446","https://openalex.org/W4312767611","https://openalex.org/W4313036808","https://openalex.org/W4319066192","https://openalex.org/W4365790371","https://openalex.org/W4383196772","https://openalex.org/W4387335033","https://openalex.org/W4387454669","https://openalex.org/W6679753764","https://openalex.org/W6697252871"],"related_works":["https://openalex.org/W4414692925","https://openalex.org/W7161296940","https://openalex.org/W4407168795","https://openalex.org/W7130860992","https://openalex.org/W4404183542","https://openalex.org/W4408153924","https://openalex.org/W7217220459","https://openalex.org/W4411848178","https://openalex.org/W4407079098","https://openalex.org/W4401725309"],"abstract_inverted_index":{"In":[0],"the":[1,7,16,35,106,116,143,173,213],"fast-evolving":[2],"landscape":[3],"of":[4,9,19,38,108,124,127,130,133,139,150,153,156,159,165,216],"digital":[5],"networks,":[6],"incidence":[8],"network":[10,39,217],"intrusions":[11],"has":[12],"escalated":[13],"alarmingly.":[14],"Simultaneously,":[15],"crucial":[17],"role":[18],"time":[20,63,102,138,164],"series":[21,64],"data":[22,110],"in":[23,111,186,201,208],"intrusion":[24,224],"detection":[25,45,68,113,225],"remains":[26],"largely":[27],"underappreciated,":[28],"with":[29,75],"most":[30],"systems":[31],"failing":[32],"to":[33,43,212],"capture":[34],"time-bound":[36],"nuances":[37],"traffic.":[40],"This":[41],"leads":[42],"compromised":[44],"accuracy":[46,209],"and":[47,81,92,135,161,210],"overlooked":[48],"temporal":[49,109],"patterns.":[50],"Addressing":[51],"this":[52],"gap,":[53],"we":[54,104,146],"introduce":[55],"a":[56,76,87,198,204,220],"novel":[57],"SSAE-TCN-BiLSTM":[58],"(STL)":[59],"model":[60,120,205],"that":[61,206],"integrates":[62],"analysis,":[65],"significantly":[66],"enhancing":[67],"capabilities.":[69],"Our":[70,195],"approach":[71],"reduces":[72],"feature":[73],"dimensionality":[74],"Stacked":[77],"Sparse":[78],"Autoencoder":[79],"(SSAE)":[80],"extracts":[82],"temporally":[83],"relevant":[84],"features":[85],"through":[86],"Temporal":[88],"Convolutional":[89],"Network":[90,97],"(TCN)":[91],"Bidirectional":[93],"Long":[94],"Short-term":[95],"Memory":[96],"(Bi-LSTM).":[98],"By":[99],"meticulously":[100],"adjusting":[101],"steps,":[103],"underscore":[105],"significance":[107,185],"bolstering":[112],"accuracy.":[114],"On":[115],"UNSW-NB15":[117],"dataset,":[118,145],"our":[119],"achieved":[121],"an":[122,136,148,162],"F1-score":[123,149],"99.49%,":[125],"Accuracy":[126,152],"99.43%,":[128],"Precision":[129,155],"99.38%,":[131],"Recall":[132,158],"99.60%,":[134],"inference":[137,163],"4.24":[140],"s.":[141,167],"For":[142],"CICDS2017":[144],"recorded":[147],"99.53%,":[151],"99.62%,":[154],"99.27%,":[157],"99.79%,":[160],"5.72":[166],"These":[168],"findings":[169],"not":[170],"only":[171],"confirm":[172],"STL":[174],"model\u2019s":[175],"superior":[176],"performance":[177],"but":[178],"also":[179],"its":[180,184],"operational":[181],"efficiency,":[182],"underpinning":[183],"real-world":[187],"cybersecurity":[188],"scenarios":[189],"where":[190],"rapid":[191],"response":[192],"is":[193],"paramount.":[194],"contribution":[196],"represents":[197],"significant":[199],"advance":[200],"cybersecurity,":[202],"proposing":[203],"excels":[207],"adaptability":[211],"dynamic":[214],"nature":[215],"traffic,":[218],"setting":[219],"new":[221],"benchmark":[222],"for":[223],"systems.":[226]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":3}],"updated_date":"2026-11-10T11:39:43.021295","created_date":"2023-12-29T00:00:00"}