{"id":"https://openalex.org/W3195279647","doi":"https://doi.org/10.1109/tvt.2021.3106940","title":"Anomaly Detection for In-Vehicle Network Using CNN-LSTM With Attention Mechanism","display_name":"Anomaly Detection for In-Vehicle Network Using CNN-LSTM With Attention Mechanism","publication_year":2021,"publication_date":"2021-08-24","ids":{"openalex":"https://openalex.org/W3195279647","doi":"https://doi.org/10.1109/tvt.2021.3106940","mag":"3195279647"},"language":"en","primary_location":{"id":"doi:10.1109/tvt.2021.3106940","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tvt.2021.3106940","pdf_url":null,"source":{"id":"https://openalex.org/S10936095","display_name":"IEEE Transactions on Vehicular Technology","issn_l":"0018-9545","issn":["0018-9545","1939-9359"],"is_oa":false,"is_in_doaj":false,"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 Transactions on Vehicular Technology","raw_type":"journal-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/A5024928963","display_name":"Heng Sun","orcid":"https://orcid.org/0000-0001-9027-1119"},"institutions":[{"id":"https://openalex.org/I159948400","display_name":"Jinan University","ror":"https://ror.org/02xe5ns62","country_code":"CN","type":"education","lineage":["https://openalex.org/I159948400"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Heng Sun","raw_affiliation_strings":["College of Information Science and Technology, Jinan University, Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0001-9027-1119","affiliations":[{"raw_affiliation_string":"College of Information Science and Technology, Jinan University, Guangzhou, China","institution_ids":["https://openalex.org/I159948400"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100700408","display_name":"Miaomiao Chen","orcid":"https://orcid.org/0000-0001-7420-8275"},"institutions":[{"id":"https://openalex.org/I159948400","display_name":"Jinan University","ror":"https://ror.org/02xe5ns62","country_code":"CN","type":"education","lineage":["https://openalex.org/I159948400"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Miaomiao Chen","raw_affiliation_strings":["College of Information Science and Technology, Jinan University, Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0001-7420-8275","affiliations":[{"raw_affiliation_string":"College of Information Science and Technology, Jinan University, Guangzhou, China","institution_ids":["https://openalex.org/I159948400"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082041657","display_name":"Jian Weng","orcid":null},"institutions":[{"id":"https://openalex.org/I159948400","display_name":"Jinan University","ror":"https://ror.org/02xe5ns62","country_code":"CN","type":"education","lineage":["https://openalex.org/I159948400"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jian Weng","raw_affiliation_strings":["College of Information Science and Technology, Jinan University, Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0003-4067-8230","affiliations":[{"raw_affiliation_string":"College of Information Science and Technology, Jinan University, Guangzhou, China","institution_ids":["https://openalex.org/I159948400"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062494507","display_name":"Zhiquan Liu","orcid":"https://orcid.org/0000-0002-3934-2177"},"institutions":[{"id":"https://openalex.org/I159948400","display_name":"Jinan University","ror":"https://ror.org/02xe5ns62","country_code":"CN","type":"education","lineage":["https://openalex.org/I159948400"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhiquan Liu","raw_affiliation_strings":["College of Information Science and Technology, Jinan University, Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-3934-2177","affiliations":[{"raw_affiliation_string":"College of Information Science and Technology, Jinan University, Guangzhou, China","institution_ids":["https://openalex.org/I159948400"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5035643851","display_name":"Guanggang Geng","orcid":"https://orcid.org/0000-0002-9039-6883"},"institutions":[{"id":"https://openalex.org/I159948400","display_name":"Jinan University","ror":"https://ror.org/02xe5ns62","country_code":"CN","type":"education","lineage":["https://openalex.org/I159948400"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guanggang Geng","raw_affiliation_strings":["College of Information Science and Technology, Jinan University, Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-9039-6883","affiliations":[{"raw_affiliation_string":"College of Information Science and Technology, Jinan University, Guangzhou, China","institution_ids":["https://openalex.org/I159948400"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5024928963"],"corresponding_institution_ids":["https://openalex.org/I159948400"],"apc_list":null,"apc_paid":null,"fwci":14.4113,"has_fulltext":false,"cited_by_count":182,"citation_normalized_percentile":{"value":0.99161027,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":"70","issue":"10","first_page":"10880","last_page":"10893"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9995999932289124,"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"}},"topics":[{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9995999932289124,"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/T10400","display_name":"Network Security and Intrusion Detection","score":0.9994000196456909,"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/T10761","display_name":"Vehicular Ad Hoc Networks (VANETs)","score":0.9976999759674072,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.6290189027786255},{"id":"https://openalex.org/keywords/mechanism","display_name":"Mechanism (biology)","score":0.5871661305427551},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5722300410270691},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.519224226474762},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.4448195993900299},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.33187806606292725},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.13369140028953552}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.6290189027786255},{"id":"https://openalex.org/C89611455","wikidata":"https://www.wikidata.org/wiki/Q6804646","display_name":"Mechanism (biology)","level":2,"score":0.5871661305427551},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5722300410270691},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.519224226474762},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.4448195993900299},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.33187806606292725},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.13369140028953552},{"id":"https://openalex.org/C26873012","wikidata":"https://www.wikidata.org/wiki/Q214781","display_name":"Condensed matter physics","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tvt.2021.3106940","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tvt.2021.3106940","pdf_url":null,"source":{"id":"https://openalex.org/S10936095","display_name":"IEEE Transactions on Vehicular Technology","issn_l":"0018-9545","issn":["0018-9545","1939-9359"],"is_oa":false,"is_in_doaj":false,"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 Transactions on Vehicular Technology","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1427926582","display_name":null,"funder_award_id":"U1736203","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2333936041","display_name":null,"funder_award_id":"61802146","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W1924770834","https://openalex.org/W2276974676","https://openalex.org/W2561208905","https://openalex.org/W2739928414","https://openalex.org/W2766761849","https://openalex.org/W2790864385","https://openalex.org/W2806407694","https://openalex.org/W2891789701","https://openalex.org/W2900471328","https://openalex.org/W2906515322","https://openalex.org/W2921143934","https://openalex.org/W2928675906","https://openalex.org/W2932010921","https://openalex.org/W2948384692","https://openalex.org/W2997711900","https://openalex.org/W2998500054","https://openalex.org/W3007940922","https://openalex.org/W3026999846","https://openalex.org/W3034885317","https://openalex.org/W3043071944","https://openalex.org/W3120195926","https://openalex.org/W3130982961","https://openalex.org/W3133742803","https://openalex.org/W3134606046","https://openalex.org/W4394652202","https://openalex.org/W6640212811","https://openalex.org/W6694503590"],"related_works":["https://openalex.org/W2806741695","https://openalex.org/W4290647774","https://openalex.org/W3189286258","https://openalex.org/W3207797160","https://openalex.org/W3210364259","https://openalex.org/W4300558037","https://openalex.org/W2667207928","https://openalex.org/W2912112202","https://openalex.org/W4377864969","https://openalex.org/W2033914206"],"abstract_inverted_index":{"With":[0],"the":[1,5,11,28,65,79,83,94,103,108,113,129,141,145,154,164,175,214,220],"increasing":[2],"connectivity":[3],"between":[4],"Electronic":[6],"Control":[7],"Units":[8],"(ECUs)":[9],"and":[10,15,90,122,147,179,206],"outside":[12],"world,":[13],"safety":[14],"security":[16,37],"have":[17],"become":[18],"stringent":[19],"problems.":[20],"The":[21,70,150,185],"Controller":[22],"Area":[23],"Network":[24],"(CAN)":[25],"bus":[26],"is":[27,43,180,225],"most":[29],"commonly":[30],"used":[31],"in-vehicle":[32,66],"network":[33],"protocol,":[34],"which":[35,138],"lacks":[36],"mechanisms":[38],"by":[39,120,227],"design,":[40],"so":[41,127,168],"it":[42,92,169],"vulnerable":[44],"to":[45,77,101,158,173],"various":[46],"attacks.":[47],"In":[48],"this":[49],"paper,":[50],"we":[51,111],"propose":[52],"a":[53,124],"novel":[54],"intrusion":[55],"detection":[56,224],"model":[57,62,72,130,146,152,194],"called":[58],"CNN-LSTM":[59],"with":[60,200,213],"Attention":[61],"(CLAM)":[63],"for":[64,182],"network,":[67],"especially":[68],"CAN.":[69],"CLAM":[71,193],"uses":[73,153],"one-dimensional":[74],"convolution":[75],"(Conv1D)":[76],"extract":[78,102,159],"abstract":[80],"features":[81],"of":[82,115,144,204,210,222],"signal":[84,161],"values":[85],"at":[86],"each":[87,116],"time":[88,104,136],"step":[89],"feeds":[91],"into":[93],"Bidirectional":[95],"Long":[96],"Short":[97],"Term":[98],"Memory":[99],"(Bi-LSTM)":[100],"dependence.":[105],"By":[106],"combining":[107],"attention":[109],"mechanisms,":[110],"calculate":[112],"weight":[114],"hidden":[117],"state":[118],"output":[119],"Bi-LSTM":[121],"perform":[123],"weighted":[125],"summation,":[126],"that":[128,190],"focuses":[131],"only":[132],"on":[133],"locally":[134],"important":[135],"steps,":[137],"can":[139,195],"improve":[140],"convergence":[142],"speed":[143],"prediction":[148],"accuracy.":[149],"proposed":[151,192],"bit":[155],"flip":[156],"rate":[157,209],"continuous":[160],"boundaries":[162],"in":[163,216],"64-bit":[165],"CAN":[166,176,198],"data,":[167],"does":[170],"not":[171],"need":[172],"parse":[174],"communication":[177],"matrix":[178],"suitable":[181],"different":[183],"vehicles.":[184],"extensive":[186],"evaluation":[187],"results":[188],"demonstrate":[189],"our":[191],"effectively":[196],"detect":[197],"attacks,":[199],"an":[201,207],"average":[202],"F1-score":[203],"0.951":[205],"error":[208],"2.16%.":[211],"Compared":[212],"work":[215],"related":[217],"research":[218],"fields,":[219],"accuracy":[221],"attack":[223],"improved":[226],"2.5%.":[228]},"counts_by_year":[{"year":2026,"cited_by_count":21},{"year":2025,"cited_by_count":58},{"year":2024,"cited_by_count":46},{"year":2023,"cited_by_count":42},{"year":2022,"cited_by_count":14},{"year":2021,"cited_by_count":1}],"updated_date":"2026-05-26T13:28:51.108037","created_date":"2025-10-10T00:00:00"}