{"id":"https://openalex.org/W4323797442","doi":"https://doi.org/10.1109/access.2023.3254915","title":"NIDS-CNNLSTM: Network Intrusion Detection Classification Model Based on Deep Learning","display_name":"NIDS-CNNLSTM: Network Intrusion Detection Classification Model Based on Deep Learning","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4323797442","doi":"https://doi.org/10.1109/access.2023.3254915"},"language":"en","primary_location":{"id":"doi:10.1109/access.2023.3254915","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2023.3254915","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10064274.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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 Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10064274.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5039865987","display_name":"Jiawei Du","orcid":"https://orcid.org/0000-0002-5186-5595"},"institutions":[{"id":"https://openalex.org/I4210162994","display_name":"Xijing University","ror":"https://ror.org/05xsjkb63","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210162994"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiawei Du","raw_affiliation_strings":["School of Computer Science, Xijing University, Xi&#x2019;an, China"],"raw_orcid":"https://orcid.org/0000-0002-5186-5595","affiliations":[{"raw_affiliation_string":"School of Computer Science, Xijing University, Xi&#x2019;an, China","institution_ids":["https://openalex.org/I4210162994"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016763727","display_name":"Kai Yang","orcid":"https://orcid.org/0000-0002-7338-3765"},"institutions":[{"id":"https://openalex.org/I4210162994","display_name":"Xijing University","ror":"https://ror.org/05xsjkb63","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210162994"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kai Yang","raw_affiliation_strings":["School of Computer Science, Xijing University, Xi&#x2019;an, China"],"raw_orcid":"https://orcid.org/0000-0002-7338-3765","affiliations":[{"raw_affiliation_string":"School of Computer Science, Xijing University, Xi&#x2019;an, China","institution_ids":["https://openalex.org/I4210162994"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101523603","display_name":"Yanjing Hu","orcid":"https://orcid.org/0000-0003-1278-0596"},"institutions":[{"id":"https://openalex.org/I4210132990","display_name":"State Key Laboratory of Cryptology","ror":"https://ror.org/02pn5rj08","country_code":"CN","type":"government","lineage":["https://openalex.org/I4210132990"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanjing Hu","raw_affiliation_strings":["School of Cryptographic Engineering, Engineering University of PAP, Xi&#x2019;an, China"],"raw_orcid":"https://orcid.org/0000-0003-1278-0596","affiliations":[{"raw_affiliation_string":"School of Cryptographic Engineering, Engineering University of PAP, Xi&#x2019;an, China","institution_ids":["https://openalex.org/I4210132990"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5067616704","display_name":"Lingjie Jiang","orcid":"https://orcid.org/0000-0002-8926-3063"},"institutions":[{"id":"https://openalex.org/I4210162994","display_name":"Xijing University","ror":"https://ror.org/05xsjkb63","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210162994"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lingjie Jiang","raw_affiliation_strings":["School of Electronic Information, Xijing University, Xi&#x2019;an, China"],"raw_orcid":"https://orcid.org/0000-0002-8926-3063","affiliations":[{"raw_affiliation_string":"School of Electronic Information, Xijing University, Xi&#x2019;an, China","institution_ids":["https://openalex.org/I4210162994"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":22.3796,"has_fulltext":true,"cited_by_count":118,"citation_normalized_percentile":{"value":0.99737124,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"11","issue":null,"first_page":"24808","last_page":"24821"},"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/T11598","display_name":"Internet Traffic Analysis and Secure E-voting","score":0.9994000196456909,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9990000128746033,"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":0.86843341588974},{"id":"https://openalex.org/keywords/intrusion-detection-system","display_name":"Intrusion detection system","score":0.6605049967765808},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6364138126373291},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5879454016685486},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5431811213493347},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5429695248603821},{"id":"https://openalex.org/keywords/binary-classification","display_name":"Binary classification","score":0.5321592092514038},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.508922815322876},{"id":"https://openalex.org/keywords/constant-false-alarm-rate","display_name":"Constant false alarm rate","score":0.48084649443626404},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.44352415204048157},{"id":"https://openalex.org/keywords/network-security","display_name":"Network security","score":0.4277944564819336},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.2176639437675476},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.09602943062782288}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.86843341588974},{"id":"https://openalex.org/C35525427","wikidata":"https://www.wikidata.org/wiki/Q745881","display_name":"Intrusion detection system","level":2,"score":0.6605049967765808},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6364138126373291},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5879454016685486},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5431811213493347},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5429695248603821},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.5321592092514038},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.508922815322876},{"id":"https://openalex.org/C77052588","wikidata":"https://www.wikidata.org/wiki/Q644307","display_name":"Constant false alarm rate","level":2,"score":0.48084649443626404},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.44352415204048157},{"id":"https://openalex.org/C182590292","wikidata":"https://www.wikidata.org/wiki/Q989632","display_name":"Network security","level":2,"score":0.4277944564819336},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.2176639437675476},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.09602943062782288}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2023.3254915","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2023.3254915","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10064274.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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 Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:832fdf13ace94c539e9f203b2888540a","is_oa":true,"landing_page_url":"https://doaj.org/article/832fdf13ace94c539e9f203b2888540a","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 11, Pp 24808-24821 (2023)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2023.3254915","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2023.3254915","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10064274.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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 Access","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.4300000071525574}],"awards":[],"funders":[{"id":"https://openalex.org/F4320329602","display_name":"Xijing University","ror":"https://ror.org/05xsjkb63"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4323797442.pdf","grobid_xml":"https://content.openalex.org/works/W4323797442.grobid-xml"},"referenced_works_count":35,"referenced_works":["https://openalex.org/W1519683338","https://openalex.org/W1811853421","https://openalex.org/W2326324325","https://openalex.org/W2431723848","https://openalex.org/W2766882809","https://openalex.org/W2775103799","https://openalex.org/W2889165715","https://openalex.org/W2891214000","https://openalex.org/W2909251399","https://openalex.org/W2918109107","https://openalex.org/W2921453769","https://openalex.org/W2942788712","https://openalex.org/W2944992190","https://openalex.org/W3003525894","https://openalex.org/W3005630930","https://openalex.org/W3025125719","https://openalex.org/W3027236250","https://openalex.org/W3087728398","https://openalex.org/W3101647584","https://openalex.org/W3125540236","https://openalex.org/W3126814579","https://openalex.org/W3190260840","https://openalex.org/W3197913358","https://openalex.org/W4200037416","https://openalex.org/W4200116821","https://openalex.org/W4206268789","https://openalex.org/W4211210065","https://openalex.org/W4212986808","https://openalex.org/W4220790513","https://openalex.org/W4220982217","https://openalex.org/W4230861824","https://openalex.org/W4295788744","https://openalex.org/W4303422666","https://openalex.org/W6631214613","https://openalex.org/W6701537683"],"related_works":["https://openalex.org/W2061466315","https://openalex.org/W2376886931","https://openalex.org/W2010561419","https://openalex.org/W2374845301","https://openalex.org/W2351448539","https://openalex.org/W1977863481","https://openalex.org/W2384741105","https://openalex.org/W2185594426","https://openalex.org/W3157271777","https://openalex.org/W2377372927"],"abstract_inverted_index":{"Intrusion":[0],"detection":[1,12,35,177],"is":[2,43,117,147,192],"the":[3,10,24,46,51,67,70,75,79,96,100,106,134,144,166,202],"core":[4],"topic":[5],"of":[6,26,50,54,69,74,83,153,158],"network":[7,27,33,62,199],"security,":[8],"and":[9,60,65,72,94,104,112,123,130,141,143,179,182,197],"intrusion":[11,34],"algorithm":[13],"based":[14,39,108],"on":[15,40,109,133],"deep":[16,41],"learning":[17,42,81],"has":[18,160],"become":[19],"a":[20,32,175,183],"research":[21],"hotspot":[22],"in":[23,89,169,201],"field":[25],"security.":[28],"In":[29],"this":[30],"paper,":[31],"classification":[36,111,180],"model":[37,116],"(NIDS-CNNLSTM)":[38],"constructed":[44],"for":[45,195],"wireless":[47],"sensing":[48],"scenario":[49],"Industrial":[52],"Internet":[53],"Things":[55],"(IIoT)":[56],"to":[57],"effectively":[58],"distinguish":[59],"identify":[61],"traffic":[63],"data":[64,200],"ensure":[66],"security":[68],"equipment":[71],"operation":[73],"IIoT.":[76,203],"NIDS-CNNLSTM":[77,159],"combines":[78],"powerful":[80],"ability":[82],"long":[84],"short-term":[85],"memory":[86],"neural":[87,102],"networks":[88],"time":[90],"series":[91],"data,":[92],"learns":[93],"classifies":[95],"features":[97],"selected":[98],"by":[99],"convolutional":[101],"network,":[103],"verifies":[105],"applicability":[107],"binary":[110],"multi-classification":[113],"scenarios.":[114],"The":[115,127,155,172],"trained":[118],"using":[119],"KDD":[120],"CUP99,":[121],"NSL_KDD":[122],"UNSW_NB15":[124],"classic":[125],"datasets.":[126],"verification":[128],"accuracy":[129,145,181],"training":[131],"loss":[132],"three":[135],"datasets":[136],"all":[137],"show":[138],"good":[139],"convergence":[140],"level,":[142],"rate":[146,178,187],"high":[148,176],"when":[149],"classifying":[150],"various":[151],"types":[152],"traffic.":[154],"overall":[156],"performance":[157],"been":[161],"significantly":[162],"improved":[163],"compared":[164],"with":[165],"models":[167],"proposed":[168],"previous":[170],"studies.":[171],"effectiveness":[173],"shows":[174],"low":[184],"false":[185],"alarm":[186],"through":[188],"experimental":[189],"results.":[190],"It":[191],"more":[193],"suitable":[194],"large-scale":[196],"multi-scenario":[198]},"counts_by_year":[{"year":2026,"cited_by_count":17},{"year":2025,"cited_by_count":42},{"year":2024,"cited_by_count":45},{"year":2023,"cited_by_count":14}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
