{"id":"https://openalex.org/W3134396348","doi":"https://doi.org/10.1109/besc51023.2020.9348310","title":"An Efficient Intrusion Detection Model Combined Bidirectional Gated Recurrent Units With Attention Mechanism","display_name":"An Efficient Intrusion Detection Model Combined Bidirectional Gated Recurrent Units With Attention Mechanism","publication_year":2020,"publication_date":"2020-11-05","ids":{"openalex":"https://openalex.org/W3134396348","doi":"https://doi.org/10.1109/besc51023.2020.9348310","mag":"3134396348"},"language":"en","primary_location":{"id":"doi:10.1109/besc51023.2020.9348310","is_oa":false,"landing_page_url":"https://doi.org/10.1109/besc51023.2020.9348310","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 7th International Conference on Behavioural and Social Computing (BESC)","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/A5100319491","display_name":"Jingyi Wang","orcid":"https://orcid.org/0000-0001-7113-7635"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jingyi Wang","raw_affiliation_strings":["School of Computer and Information Technology Beijing Jiaotong University,Beijing,China","School of Computer and Information Technology Beijing Jiaotong University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Computer and Information Technology Beijing Jiaotong University,Beijing,China","institution_ids":["https://openalex.org/I21193070"]},{"raw_affiliation_string":"School of Computer and Information Technology Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049441487","display_name":"Naiyue Chen","orcid":"https://orcid.org/0000-0001-9681-9405"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Naiyue Chen","raw_affiliation_strings":["School of Computer and Information Technology Beijing Jiaotong University,Beijing,China","School of Computer and Information Technology Beijing Jiaotong University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Computer and Information Technology Beijing Jiaotong University,Beijing,China","institution_ids":["https://openalex.org/I21193070"]},{"raw_affiliation_string":"School of Computer and Information Technology Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5104160885","display_name":"Jinhui Yu","orcid":null},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinhui Yu","raw_affiliation_strings":["School of Computer and Information Technology Beijing Jiaotong University,Beijing,China","School of Computer and Information Technology Beijing Jiaotong University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Computer and Information Technology Beijing Jiaotong University,Beijing,China","institution_ids":["https://openalex.org/I21193070"]},{"raw_affiliation_string":"School of Computer and Information Technology Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033896100","display_name":"Yi Jin","orcid":"https://orcid.org/0000-0001-8408-3816"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yi Jin","raw_affiliation_strings":["School of Computer and Information Technology Beijing Jiaotong University,Beijing,China","School of Computer and Information Technology Beijing Jiaotong University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Computer and Information Technology Beijing Jiaotong University,Beijing,China","institution_ids":["https://openalex.org/I21193070"]},{"raw_affiliation_string":"School of Computer and Information Technology Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5010019122","display_name":"Yidong Li","orcid":"https://orcid.org/0000-0003-2965-6196"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yidong Li","raw_affiliation_strings":["School of Computer and Information Technology Beijing Jiaotong University,Beijing,China","School of Computer and Information Technology Beijing Jiaotong University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Computer and Information Technology Beijing Jiaotong University,Beijing,China","institution_ids":["https://openalex.org/I21193070"]},{"raw_affiliation_string":"School of Computer and Information Technology Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5100319491"],"corresponding_institution_ids":["https://openalex.org/I21193070"],"apc_list":null,"apc_paid":null,"fwci":0.4625,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.68759232,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"72","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/T11598","display_name":"Internet Traffic Analysis and Secure E-voting","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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9965999722480774,"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":0.8278864622116089},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.7295674085617065},{"id":"https://openalex.org/keywords/intrusion-detection-system","display_name":"Intrusion detection system","score":0.7018487453460693},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6858680844306946},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6477659940719604},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.57057785987854},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5045100450515747},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4961465299129486},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.45702219009399414},{"id":"https://openalex.org/keywords/constant-false-alarm-rate","display_name":"Constant false alarm rate","score":0.4465022683143616},{"id":"https://openalex.org/keywords/false-alarm","display_name":"False alarm","score":0.43918377161026},{"id":"https://openalex.org/keywords/mechanism","display_name":"Mechanism (biology)","score":0.43752095103263855},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.42340797185897827},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.415035218000412}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8278864622116089},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.7295674085617065},{"id":"https://openalex.org/C35525427","wikidata":"https://www.wikidata.org/wiki/Q745881","display_name":"Intrusion detection system","level":2,"score":0.7018487453460693},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6858680844306946},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6477659940719604},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.57057785987854},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5045100450515747},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4961465299129486},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.45702219009399414},{"id":"https://openalex.org/C77052588","wikidata":"https://www.wikidata.org/wiki/Q644307","display_name":"Constant false alarm rate","level":2,"score":0.4465022683143616},{"id":"https://openalex.org/C2776836416","wikidata":"https://www.wikidata.org/wiki/Q1364844","display_name":"False alarm","level":2,"score":0.43918377161026},{"id":"https://openalex.org/C89611455","wikidata":"https://www.wikidata.org/wiki/Q6804646","display_name":"Mechanism (biology)","level":2,"score":0.43752095103263855},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.42340797185897827},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.415035218000412},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"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/besc51023.2020.9348310","is_oa":false,"landing_page_url":"https://doi.org/10.1109/besc51023.2020.9348310","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 7th International Conference on Behavioural and Social Computing (BESC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","score":0.5899999737739563,"display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W2009058336","https://openalex.org/W2110798204","https://openalex.org/W2267339884","https://openalex.org/W2296509296","https://openalex.org/W2335999708","https://openalex.org/W2345946136","https://openalex.org/W2762776925","https://openalex.org/W2853755454","https://openalex.org/W2909665293","https://openalex.org/W2924353071","https://openalex.org/W2941716987","https://openalex.org/W2963042536","https://openalex.org/W6676481782"],"related_works":["https://openalex.org/W3013693939","https://openalex.org/W2159052453","https://openalex.org/W2566616303","https://openalex.org/W3131327266","https://openalex.org/W1983393909","https://openalex.org/W2040150569","https://openalex.org/W2468095590","https://openalex.org/W2132174924","https://openalex.org/W1911540634","https://openalex.org/W2013909972"],"abstract_inverted_index":{"In":[0,78],"recent":[1],"years,":[2],"various":[3],"types":[4],"of":[5,13,54,69,107,169],"network":[6,14,119,159],"attacks":[7,38],"emerge":[8],"in":[9,57,66,137],"endlessly,":[10],"the":[11,51,67,135,152],"protection":[12],"security":[15],"has":[16,42],"been":[17],"paid":[18],"more":[19,21],"and":[20,39,59,76,162,178],"attention":[22,93,121],"by":[23,147],"our":[24],"society.":[25],"Network":[26],"Intrusion":[27],"Detection":[28],"System":[29],"(NIDS)is":[30],"used":[31],"to":[32,50,102,123],"protect":[33],"computer":[34],"systems":[35],"from":[36],"malicious":[37],"intrusions,":[40],"thus":[41],"also":[43],"become":[44],"a":[45,82,98,113,131],"hot":[46],"research":[47],"field.":[48],"Due":[49],"great":[52],"success":[53],"deep":[55,70,89],"learning":[56,71,90],"industry":[58],"academia,":[60],"there":[61],"is":[62],"an":[63],"increasing":[64],"interest":[65],"application":[68],"methods":[72,166],"for":[73],"feature":[74,105],"representations":[75,106],"classification.":[77],"this":[79],"paper,we":[80],"propose":[81],"intrusion":[83,108],"detection":[84],"model":[85,156],"based":[86],"on":[87,130],"time-related":[88],"approach":[91],"with":[92,120,167],"mechanism.":[94],"Firstly,":[95],"we":[96,111],"build":[97],"stacked":[99],"sparse":[100,144],"autoencoder(SSAE)":[101],"extract":[103],"high-level":[104],"information.":[109],"Then":[110],"design":[112],"two-layer":[114],"bidirectional":[115],"gated":[116],"recurrent":[117],"unit(BiGRU)":[118],"mechanism":[122],"classify":[124],"traffic":[125],"data.":[126],"We":[127],"perform":[128],"experiments":[129],"benchmark":[132],"dataset":[133],"UNSW-NB15,":[134],"results":[136],"binary":[138],"classification":[139,153],"indicate":[140],"that":[141],"using":[142],"high-dimensional":[143],"features":[145],"extracted":[146],"SSAE":[148],"can":[149,157],"significantly":[150],"accelerate":[151],"progress.":[154],"Our":[155],"detect":[158],"intrusions":[160],"effectively":[161],"outperform":[163],"other":[164],"related":[165],"reduction":[168],"false":[170],"alarm":[171],"rate,":[172,175],"high":[173],"accuracy":[174],"reduced":[176],"training":[177],"testing":[179],"time.":[180]},"counts_by_year":[{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
