{"id":"https://openalex.org/W4382203413","doi":"https://doi.org/10.1109/tits.2023.3286611","title":"Deep Learning-Based Anomaly Detection for Connected Autonomous Vehicles Using Spatiotemporal Information","display_name":"Deep Learning-Based Anomaly Detection for Connected Autonomous Vehicles Using Spatiotemporal Information","publication_year":2023,"publication_date":"2023-06-27","ids":{"openalex":"https://openalex.org/W4382203413","doi":"https://doi.org/10.1109/tits.2023.3286611"},"language":"en","primary_location":{"id":"doi:10.1109/tits.2023.3286611","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2023.3286611","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"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 Intelligent Transportation Systems","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/A5092075937","display_name":"Pegah Mansourian","orcid":null},"institutions":[{"id":"https://openalex.org/I74413500","display_name":"University of Windsor","ror":"https://ror.org/01gw3d370","country_code":"CA","type":"education","lineage":["https://openalex.org/I74413500"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Pegah Mansourian","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Windsor, Windsor, Canada"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Windsor, Windsor, Canada","institution_ids":["https://openalex.org/I74413500"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100404886","display_name":"Ning Zhang","orcid":"https://orcid.org/0000-0002-8781-4925"},"institutions":[{"id":"https://openalex.org/I74413500","display_name":"University of Windsor","ror":"https://ror.org/01gw3d370","country_code":"CA","type":"education","lineage":["https://openalex.org/I74413500"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Ning Zhang","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Windsor, Windsor, Canada"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Windsor, Windsor, Canada","institution_ids":["https://openalex.org/I74413500"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055035963","display_name":"Arunita Jaekel","orcid":"https://orcid.org/0000-0001-6836-9670"},"institutions":[{"id":"https://openalex.org/I74413500","display_name":"University of Windsor","ror":"https://ror.org/01gw3d370","country_code":"CA","type":"education","lineage":["https://openalex.org/I74413500"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Arunita Jaekel","raw_affiliation_strings":["Department of Computer Science, University of Windsor, Windsor, Canada"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Windsor, Windsor, Canada","institution_ids":["https://openalex.org/I74413500"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5001954053","display_name":"Marc Kneppers","orcid":null},"institutions":[{"id":"https://openalex.org/I2800934870","display_name":"Telus (Canada)","ror":"https://ror.org/040vbkv27","country_code":"CA","type":"company","lineage":["https://openalex.org/I2800934870"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Marc Kneppers","raw_affiliation_strings":["TELUS, Vancouver, Canada"],"affiliations":[{"raw_affiliation_string":"TELUS, Vancouver, Canada","institution_ids":["https://openalex.org/I2800934870"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5092075937"],"corresponding_institution_ids":["https://openalex.org/I74413500"],"apc_list":null,"apc_paid":null,"fwci":10.2318,"has_fulltext":false,"cited_by_count":78,"citation_normalized_percentile":{"value":0.98836697,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":"24","issue":"12","first_page":"16006","last_page":"16017"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10761","display_name":"Vehicular Ad Hoc Networks (VANETs)","score":0.9994000196456909,"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"}},"topics":[{"id":"https://openalex.org/T10761","display_name":"Vehicular Ad Hoc Networks (VANETs)","score":0.9994000196456909,"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"}},{"id":"https://openalex.org/T10400","display_name":"Network Security and Intrusion Detection","score":0.9805999994277954,"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":0.9781000018119812,"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":0.7400165796279907},{"id":"https://openalex.org/keywords/intrusion-detection-system","display_name":"Intrusion detection system","score":0.5581480264663696},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.5144479274749756},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.512624204158783},{"id":"https://openalex.org/keywords/naive-bayes-classifier","display_name":"Naive Bayes classifier","score":0.48809540271759033},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.47039684653282166},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.4172561764717102},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4139215350151062},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.3630422055721283},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3211050033569336},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.2337041199207306}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7400165796279907},{"id":"https://openalex.org/C35525427","wikidata":"https://www.wikidata.org/wiki/Q745881","display_name":"Intrusion detection system","level":2,"score":0.5581480264663696},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.5144479274749756},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.512624204158783},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.48809540271759033},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.47039684653282166},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.4172561764717102},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4139215350151062},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3630422055721283},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3211050033569336},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.2337041199207306}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tits.2023.3286611","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2023.3286611","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"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 Intelligent Transportation Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","score":0.46000000834465027,"id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320322675","display_name":"Mitacs","ror":"https://ror.org/00cjrc276"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W2064675550","https://openalex.org/W2116520617","https://openalex.org/W2133854595","https://openalex.org/W2461378669","https://openalex.org/W2484214139","https://openalex.org/W2520418590","https://openalex.org/W2545810962","https://openalex.org/W2561208905","https://openalex.org/W2739928414","https://openalex.org/W2784168723","https://openalex.org/W2790241535","https://openalex.org/W2928675906","https://openalex.org/W2948384692","https://openalex.org/W2959120033","https://openalex.org/W2962444737","https://openalex.org/W2964029596","https://openalex.org/W2970148885","https://openalex.org/W2977724618","https://openalex.org/W2979202956","https://openalex.org/W2989869976","https://openalex.org/W3005206852","https://openalex.org/W3042693420","https://openalex.org/W3043071944","https://openalex.org/W3105105644","https://openalex.org/W3106212564","https://openalex.org/W3130982961","https://openalex.org/W3171142246","https://openalex.org/W3195279647","https://openalex.org/W3199155356","https://openalex.org/W3206867092","https://openalex.org/W4229029284","https://openalex.org/W4293057825","https://openalex.org/W4311277248","https://openalex.org/W4312529905","https://openalex.org/W6628877408"],"related_works":["https://openalex.org/W3013693939","https://openalex.org/W2566616303","https://openalex.org/W2159052453","https://openalex.org/W3186512740","https://openalex.org/W2669956259","https://openalex.org/W4249005693","https://openalex.org/W3194885736","https://openalex.org/W4363671829","https://openalex.org/W4392946183","https://openalex.org/W3088732000"],"abstract_inverted_index":{"Although":[0],"connected":[1],"mymargin":[2],"autonomous":[3],"vehicles":[4],"(CAVs)":[5],"hold":[6],"great":[7],"potential":[8],"to":[9,38,46,55,67,78,158,214,225],"improve":[10],"driving":[11],"safety":[12],"and":[13,35,61,83,101,137,181,183,190,207,217,229,233],"experience":[14],"significantly,":[15],"cybersecurity":[16],"remains":[17],"a":[18,47,94,104,141,162,193],"critical":[19],"concern.":[20],"As":[21],"the":[22,28,39,42,118,138,196,203,220],"de-facto":[23],"standard":[24],"for":[25,98,133],"in-vehicle":[26],"networks,":[27],"Controller":[29],"Area":[30],"Network":[31],"(CAN)":[32],"carries":[33],"messages":[34,145],"commands":[36],"vital":[37],"operation":[40],"of":[41,49,110,149,236],"vehicle.":[43],"However,":[44],"due":[45],"lack":[48],"security":[50],"mechanisms,":[51],"intruders":[52],"are":[53,115],"able":[54],"conduct":[56],"devastating":[57],"attacks":[58,102],"on":[59,103],"drivers":[60],"passengers":[62],"over":[63],"CAN.":[64],"In":[65],"order":[66],"safeguard":[68],"CAVs,":[69],"an":[70,89,125],"Intrusion":[71],"Detection":[72],"System":[73],"(IDS)":[74],"can":[75,230],"be":[76,226],"deployed":[77],"monitor":[79],"CAN":[80,105,135,151],"network":[81,123],"activities":[82],"detect":[84],"suspicious":[85],"behavior":[86],"resulting":[87],"from":[88],"attack.":[90],"This":[91],"paper":[92],"proposes":[93],"prediction-based":[95],"IDS":[96,169],"framework":[97],"detecting":[99],"anomalies":[100],"bus":[106],"using":[107,146,192],"temporal":[108],"correlation":[109],"message":[111],"contents.":[112],"Two":[113],"candidates":[114],"introduced":[116],"as":[117],"prediction":[119,159],"module.":[120],"The":[121,167],"first":[122],"is":[124,140,155,170,211,223],"LSTM":[126],"that":[127,143],"predicts":[128,144],"time":[129],"series":[130],"data":[131,148],"separately":[132],"each":[134],"ID,":[136],"second":[139],"ConvLSTM":[142],"correlated":[147],"several":[150],"IDs.":[152],"An":[153],"attack":[154],"classified":[156],"according":[157],"errors":[160],"by":[161],"Gaussian":[163],"Na\u00efve":[164],"Bayes":[165],"classifier.":[166],"proposed":[168,221],"evaluated":[171],"against":[172],"other":[173],"state-of-the-art":[174],"one-class":[175],"classifiers,":[176],"including":[177,187],"OCSVM,":[178],"Isolation":[179],"Forest,":[180],"Autoencoder,":[182],"three":[184],"existing":[185],"works,":[186],"ReducedInception-ResNet,":[188],"NeuroCAN,":[189],"CANLite,":[191],"real-world":[194],"dataset,":[195],"Car":[197],"Hacking":[198],"Dataset.":[199],"A":[200],"comparison":[201],"between":[202],"two":[204],"suggested":[205],"architectures":[206],"their":[208],"use":[209],"cases":[210],"given.":[212],"Compared":[213],"baseline":[215],"methods":[216],"related":[218],"studies,":[219],"method":[222],"shown":[224],"more":[227],"accurate":[228],"achieve":[231],"F-scores":[232],"detection":[234],"accuracy":[235],"almost":[237],"100%.":[238]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":44},{"year":2024,"cited_by_count":25},{"year":2023,"cited_by_count":4}],"updated_date":"2026-03-24T08:02:53.985720","created_date":"2025-10-10T00:00:00"}