{"id":"https://openalex.org/W2762776925","doi":"https://doi.org/10.1109/access.2017.2762418","title":"A Deep Learning Approach for Intrusion Detection Using Recurrent Neural Networks","display_name":"A Deep Learning Approach for Intrusion Detection Using Recurrent Neural Networks","publication_year":2017,"publication_date":"2017-01-01","ids":{"openalex":"https://openalex.org/W2762776925","doi":"https://doi.org/10.1109/access.2017.2762418","mag":"2762776925"},"language":"en","primary_location":{"id":"doi:10.1109/access.2017.2762418","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2017.2762418","pdf_url":null,"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":null,"license_id":null,"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://doi.org/10.1109/access.2017.2762418","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5071257325","display_name":"Chuanlong Yin","orcid":"https://orcid.org/0000-0003-0735-9019"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chuanlong Yin","raw_affiliation_strings":["State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou, China","ORCiD"],"raw_orcid":"https://orcid.org/0000-0003-0735-9019","affiliations":[{"raw_affiliation_string":"State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou, China","institution_ids":[]},{"raw_affiliation_string":"ORCiD","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083796252","display_name":"Yuefei Zhu","orcid":"https://orcid.org/0000-0002-9559-8783"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yuefei Zhu","raw_affiliation_strings":["State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070520850","display_name":"Jinlong Fei","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jinlong Fei","raw_affiliation_strings":["State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5003181852","display_name":"Xinzheng He","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xinzheng He","raw_affiliation_strings":["State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":2075,"currency":"USD","value_usd":2075},"apc_paid":{"value":2075,"currency":"USD","value_usd":2075},"fwci":105.5191,"has_fulltext":false,"cited_by_count":2056,"citation_normalized_percentile":{"value":0.99975943,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"5","issue":null,"first_page":"21954","last_page":"21961"},"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":0.9983000159263611,"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":0.996999979019165,"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":0.8532177209854126},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7436414957046509},{"id":"https://openalex.org/keywords/intrusion-detection-system","display_name":"Intrusion detection system","score":0.7110965847969055},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6678469777107239},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.6442047357559204},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6126197576522827},{"id":"https://openalex.org/keywords/multiclass-classification","display_name":"Multiclass classification","score":0.587307333946228},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5835575461387634},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5579288005828857},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.557245135307312},{"id":"https://openalex.org/keywords/c4.5-algorithm","display_name":"C4.5 algorithm","score":0.5426346659660339},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5373584032058716},{"id":"https://openalex.org/keywords/binary-classification","display_name":"Binary classification","score":0.4833478629589081},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3688245415687561},{"id":"https://openalex.org/keywords/naive-bayes-classifier","display_name":"Naive Bayes classifier","score":0.08756619691848755}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8532177209854126},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7436414957046509},{"id":"https://openalex.org/C35525427","wikidata":"https://www.wikidata.org/wiki/Q745881","display_name":"Intrusion detection system","level":2,"score":0.7110965847969055},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6678469777107239},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.6442047357559204},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6126197576522827},{"id":"https://openalex.org/C123860398","wikidata":"https://www.wikidata.org/wiki/Q6934605","display_name":"Multiclass classification","level":3,"score":0.587307333946228},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5835575461387634},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5579288005828857},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.557245135307312},{"id":"https://openalex.org/C52003472","wikidata":"https://www.wikidata.org/wiki/Q1022655","display_name":"C4.5 algorithm","level":4,"score":0.5426346659660339},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5373584032058716},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.4833478629589081},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3688245415687561},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.08756619691848755},{"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":2,"locations":[{"id":"doi:10.1109/access.2017.2762418","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2017.2762418","pdf_url":null,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:68d19e98ae434c93a173671c5daac7a9","is_oa":true,"landing_page_url":"https://doaj.org/article/68d19e98ae434c93a173671c5daac7a9","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 5, Pp 21954-21961 (2017)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2017.2762418","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2017.2762418","pdf_url":null,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.46000000834465027,"id":"https://metadata.un.org/sdg/15","display_name":"Life in Land"}],"awards":[{"id":"https://openalex.org/G4057063574","display_name":null,"funder_award_id":"2016YFB0801601","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"},{"id":"https://openalex.org/G6326871545","display_name":null,"funder_award_id":"2016YFB0801505","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"}],"funders":[{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W1408639475","https://openalex.org/W1871385855","https://openalex.org/W1995678176","https://openalex.org/W2066877142","https://openalex.org/W2076063813","https://openalex.org/W2084793887","https://openalex.org/W2097034581","https://openalex.org/W2099940443","https://openalex.org/W2100537916","https://openalex.org/W2120685459","https://openalex.org/W2166267120","https://openalex.org/W2287408518","https://openalex.org/W2322020277","https://openalex.org/W2342408547","https://openalex.org/W2343864402","https://openalex.org/W2344887935","https://openalex.org/W2346714907","https://openalex.org/W2399941526","https://openalex.org/W2512144135","https://openalex.org/W2547629195","https://openalex.org/W2560162835","https://openalex.org/W2655549288","https://openalex.org/W2919115771","https://openalex.org/W2991932403","https://openalex.org/W6628131027","https://openalex.org/W6696151112","https://openalex.org/W6770925153"],"related_works":["https://openalex.org/W4376528628","https://openalex.org/W1537592868","https://openalex.org/W2470590370","https://openalex.org/W3207192536","https://openalex.org/W2910954186","https://openalex.org/W1748436461","https://openalex.org/W2002271516","https://openalex.org/W2014610539","https://openalex.org/W4281721362","https://openalex.org/W2322862995"],"abstract_inverted_index":{"Intrusion":[0],"detection":[1,33,48,160],"plays":[2],"an":[3,31],"important":[4],"role":[5],"in":[6,20,62,145],"ensuring":[7],"information":[8],"security,":[9],"and":[10,39,65,68,73,100,131,148,161],"the":[11,21,57,60,69,79,82,110,155,158],"key":[12],"technology":[13],"is":[14,120,135],"to":[15,29,137],"accurately":[16],"identify":[17],"various":[18],"attacks":[19],"network.":[22],"In":[23],"this":[24],"paper,":[25],"we":[26,40,55],"explore":[27],"how":[28],"model":[30,61,127,153],"intrusion":[32,47,159,168],"system":[34],"based":[35],"on":[36,78,109],"deep":[37,43],"learning,":[38],"propose":[41],"a":[42,125,163],"learning":[44,75,103,142],"approach":[45],"for":[46,123,167],"using":[49],"recurrent":[50],"neural":[51,93],"networks":[52],"(RNN-IDS).":[53],"Moreover,":[54],"study":[56],"performance":[58,80,134],"of":[59,71,81,90,139,157],"binary":[63,147],"classification":[64,126,143],"multiclass":[66,149],"classification,":[67],"number":[70],"neurons":[72],"different":[74],"rate":[76],"impacts":[77],"proposed":[83,105],"model.":[84],"We":[85],"compare":[86],"it":[87],"with":[88,128],"those":[89],"J48,":[91],"artificial":[92],"network,":[94],"random":[95],"forest,":[96],"support":[97],"vector":[98],"machine,":[99],"other":[101],"machine":[102,141],"methods":[104,144],"by":[106],"previous":[107],"researchers":[108],"benchmark":[111],"data":[112],"set.":[113],"The":[114,151],"experimental":[115],"results":[116],"show":[117],"that":[118,132,138],"RNN-IDS":[119,152],"very":[121],"suitable":[122],"modeling":[124],"high":[129],"accuracy":[130,156],"its":[133],"superior":[136],"traditional":[140],"both":[146],"classification.":[150],"improves":[154],"provides":[162],"new":[164],"research":[165],"method":[166],"detection.":[169]},"counts_by_year":[{"year":2026,"cited_by_count":181},{"year":2025,"cited_by_count":284},{"year":2024,"cited_by_count":233},{"year":2023,"cited_by_count":258},{"year":2022,"cited_by_count":259},{"year":2021,"cited_by_count":343},{"year":2020,"cited_by_count":279},{"year":2019,"cited_by_count":160},{"year":2018,"cited_by_count":59}],"updated_date":"2026-08-27T14:10:00.468798","created_date":"2025-10-10T00:00:00"}