{"id":"https://openalex.org/W4403674397","doi":"https://doi.org/10.1109/tits.2024.3478371","title":"T-Shaped CAN Feature Integration With Lightweight Deep Learning Model for In-Vehicle Network Intrusion Detection","display_name":"T-Shaped CAN Feature Integration With Lightweight Deep Learning Model for In-Vehicle Network Intrusion Detection","publication_year":2024,"publication_date":"2024-10-23","ids":{"openalex":"https://openalex.org/W4403674397","doi":"https://doi.org/10.1109/tits.2024.3478371"},"language":"en","primary_location":{"id":"doi:10.1109/tits.2024.3478371","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2024.3478371","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/A5051619894","display_name":"Sha Huan","orcid":"https://orcid.org/0000-0002-9883-7508"},"institutions":[{"id":"https://openalex.org/I32246829","display_name":"Guangdong University of Education","ror":"https://ror.org/0574der91","country_code":"CN","type":"education","lineage":["https://openalex.org/I32246829"]},{"id":"https://openalex.org/I37987034","display_name":"Guangzhou University","ror":"https://ror.org/05ar8rn06","country_code":"CN","type":"education","lineage":["https://openalex.org/I37987034"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Sha Huan","raw_affiliation_strings":["Key Laboratory of On-Chip Communication and Sensor Chip, Guangdong Higher Education Institute, and the School of Electronics and Communication Engineering, Guangzhou University, Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-9883-7508","affiliations":[{"raw_affiliation_string":"Key Laboratory of On-Chip Communication and Sensor Chip, Guangdong Higher Education Institute, and the School of Electronics and Communication Engineering, Guangzhou University, Guangzhou, China","institution_ids":["https://openalex.org/I32246829","https://openalex.org/I37987034"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114889012","display_name":"Xiaoyi Zhang","orcid":"https://orcid.org/0009-0008-0217-1838"},"institutions":[{"id":"https://openalex.org/I4403386666","display_name":"Guangdong Technology College","ror":"https://ror.org/00f0z8g04","country_code":null,"type":"education","lineage":["https://openalex.org/I4403386666"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoyi Zhang","raw_affiliation_strings":["School of Electrical and Electronic Engineering, Guangdong Technology College, Zhaoqing, China"],"raw_orcid":"https://orcid.org/0009-0008-0217-1838","affiliations":[{"raw_affiliation_string":"School of Electrical and Electronic Engineering, Guangdong Technology College, Zhaoqing, China","institution_ids":["https://openalex.org/I4403386666"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016584004","display_name":"Wenli Shang","orcid":"https://orcid.org/0000-0001-6022-5381"},"institutions":[{"id":"https://openalex.org/I32246829","display_name":"Guangdong University of Education","ror":"https://ror.org/0574der91","country_code":"CN","type":"education","lineage":["https://openalex.org/I32246829"]},{"id":"https://openalex.org/I37987034","display_name":"Guangzhou University","ror":"https://ror.org/05ar8rn06","country_code":"CN","type":"education","lineage":["https://openalex.org/I37987034"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenli Shang","raw_affiliation_strings":["Key Laboratory of On-Chip Communication and Sensor Chip, Guangdong Higher Education Institute, and the School of Electronics and Communication Engineering, Guangzhou University, Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0001-6022-5381","affiliations":[{"raw_affiliation_string":"Key Laboratory of On-Chip Communication and Sensor Chip, Guangdong Higher Education Institute, and the School of Electronics and Communication Engineering, Guangzhou University, Guangzhou, China","institution_ids":["https://openalex.org/I32246829","https://openalex.org/I37987034"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101947366","display_name":"Haitao Cao","orcid":"https://orcid.org/0000-0002-8626-8686"},"institutions":[{"id":"https://openalex.org/I4210104080","display_name":"Guangzhou Panyu Polytechnic","ror":"https://ror.org/01j9jcf33","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210104080"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haitao Cao","raw_affiliation_strings":["School of Information Engineering, Guangzhou Panyu Polytechnic, Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-8626-8686","affiliations":[{"raw_affiliation_string":"School of Information Engineering, Guangzhou Panyu Polytechnic, Guangzhou, China","institution_ids":["https://openalex.org/I4210104080"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100338822","display_name":"Heng Li","orcid":"https://orcid.org/0000-0003-4815-0537"},"institutions":[{"id":"https://openalex.org/I32246829","display_name":"Guangdong University of Education","ror":"https://ror.org/0574der91","country_code":"CN","type":"education","lineage":["https://openalex.org/I32246829"]},{"id":"https://openalex.org/I37987034","display_name":"Guangzhou University","ror":"https://ror.org/05ar8rn06","country_code":"CN","type":"education","lineage":["https://openalex.org/I37987034"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Heng Li","raw_affiliation_strings":["Key Laboratory of On-Chip Communication and Sensor Chip, Guangdong Higher Education Institute, and the School of Electronics and Communication Engineering, Guangzhou University, Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Key Laboratory of On-Chip Communication and Sensor Chip, Guangdong Higher Education Institute, and the School of Electronics and Communication Engineering, Guangzhou University, Guangzhou, China","institution_ids":["https://openalex.org/I32246829","https://openalex.org/I37987034"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100574648","display_name":"Yuanjia Yang","orcid":null},"institutions":[{"id":"https://openalex.org/I32246829","display_name":"Guangdong University of Education","ror":"https://ror.org/0574der91","country_code":"CN","type":"education","lineage":["https://openalex.org/I32246829"]},{"id":"https://openalex.org/I37987034","display_name":"Guangzhou University","ror":"https://ror.org/05ar8rn06","country_code":"CN","type":"education","lineage":["https://openalex.org/I37987034"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuanjia Yang","raw_affiliation_strings":["Key Laboratory of On-Chip Communication and Sensor Chip, Guangdong Higher Education Institute, and the School of Electronics and Communication Engineering, Guangzhou University, Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Key Laboratory of On-Chip Communication and Sensor Chip, Guangdong Higher Education Institute, and the School of Electronics and Communication Engineering, Guangzhou University, Guangzhou, China","institution_ids":["https://openalex.org/I32246829","https://openalex.org/I37987034"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5006217763","display_name":"Wenbai Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I32246829","display_name":"Guangdong University of Education","ror":"https://ror.org/0574der91","country_code":"CN","type":"education","lineage":["https://openalex.org/I32246829"]},{"id":"https://openalex.org/I37987034","display_name":"Guangzhou University","ror":"https://ror.org/05ar8rn06","country_code":"CN","type":"education","lineage":["https://openalex.org/I37987034"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenbai Liu","raw_affiliation_strings":["Key Laboratory of On-Chip Communication and Sensor Chip, Guangdong Higher Education Institute, and the School of Electronics and Communication Engineering, Guangzhou University, Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Key Laboratory of On-Chip Communication and Sensor Chip, Guangdong Higher Education Institute, and the School of Electronics and Communication Engineering, Guangzhou University, Guangzhou, China","institution_ids":["https://openalex.org/I32246829","https://openalex.org/I37987034"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.5568,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.90400712,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":"25","issue":"12","first_page":"21183","last_page":"21196"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10400","display_name":"Network Security and Intrusion Detection","score":0.9868999719619751,"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":0.9868999719619751,"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":0.9850000143051147,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.948199987411499,"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/intrusion-detection-system","display_name":"Intrusion detection system","score":0.632684051990509},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6076087951660156},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5842936038970947},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5655480623245239},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.549888014793396},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.33010268211364746}],"concepts":[{"id":"https://openalex.org/C35525427","wikidata":"https://www.wikidata.org/wiki/Q745881","display_name":"Intrusion detection system","level":2,"score":0.632684051990509},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6076087951660156},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5842936038970947},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5655480623245239},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.549888014793396},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33010268211364746},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tits.2024.3478371","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2024.3478371","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":[],"awards":[{"id":"https://openalex.org/G2552996940","display_name":null,"funder_award_id":"2024A1515010012","funder_id":"https://openalex.org/F4320315329","funder_display_name":"Scientific Education and Research Foundation"}],"funders":[{"id":"https://openalex.org/F4320315329","display_name":"Scientific Education and Research Foundation","ror":"https://ror.org/029zvkg32"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":47,"referenced_works":["https://openalex.org/W2099005886","https://openalex.org/W2414564754","https://openalex.org/W2593182419","https://openalex.org/W2703342623","https://openalex.org/W2752900269","https://openalex.org/W2884890985","https://openalex.org/W2959120033","https://openalex.org/W2963163009","https://openalex.org/W2964262308","https://openalex.org/W2964710417","https://openalex.org/W2979202956","https://openalex.org/W2987238340","https://openalex.org/W3008798529","https://openalex.org/W3042693420","https://openalex.org/W3089289157","https://openalex.org/W3089775054","https://openalex.org/W3092232275","https://openalex.org/W3093276730","https://openalex.org/W3105105644","https://openalex.org/W3119621753","https://openalex.org/W3124913209","https://openalex.org/W3130982961","https://openalex.org/W3134606046","https://openalex.org/W3136873430","https://openalex.org/W3140476622","https://openalex.org/W3173906426","https://openalex.org/W3189306419","https://openalex.org/W3194413837","https://openalex.org/W3195279647","https://openalex.org/W3199976315","https://openalex.org/W4206309573","https://openalex.org/W4220775946","https://openalex.org/W4231725517","https://openalex.org/W4294860585","https://openalex.org/W4308605894","https://openalex.org/W4312648203","https://openalex.org/W4322487524","https://openalex.org/W4322625299","https://openalex.org/W4382203413","https://openalex.org/W4385257233","https://openalex.org/W4385682274","https://openalex.org/W4386261706","https://openalex.org/W4387757650","https://openalex.org/W4388685218","https://openalex.org/W4399385078","https://openalex.org/W6855651964","https://openalex.org/W6858838343"],"related_works":["https://openalex.org/W2731899572","https://openalex.org/W2961085424","https://openalex.org/W3215138031","https://openalex.org/W4306674287","https://openalex.org/W4321369474","https://openalex.org/W3009238340","https://openalex.org/W4360585206","https://openalex.org/W4285208911","https://openalex.org/W3046775127","https://openalex.org/W3082895349"],"abstract_inverted_index":{"With":[0],"the":[1,32,40,47,61,99,117,142],"continuous":[2],"advancement":[3],"of":[4,46],"automotive":[5],"intelligence":[6],"and":[7,43,98,116,134,165],"ubiquitous":[8],"connection,":[9],"In-Vehicle":[10],"Networks":[11],"(IVN)":[12],"are":[13,103],"confronted":[14],"with":[15,60,86],"heightened":[16],"security":[17,62],"challenges.":[18],"Attackers":[19],"can":[20],"inject":[21],"false":[22],"messages":[23],"to":[24,35,58,75,110,149],"manipulate":[25],"or":[26],"disrupt":[27],"critical":[28],"functional":[29],"modules":[30],"on":[31,137],"vehicle,":[33],"leading":[34],"significant":[36],"safety":[37],"issues.":[38],"Given":[39],"real-time":[41],"requirements":[42],"resource":[44],"constraints":[45],"in-vehicle":[48],"network,":[49],"a":[50,87,106,112,122],"lightweight":[51,88,123],"Intrusion":[52],"Detection":[53],"System":[54],"(IDS)":[55],"is":[56,119,146],"needed":[57],"deal":[59],"threats":[63],"caused":[64],"by":[65,105,121],"attacks.":[66],"This":[67],"paper":[68],"proposes":[69],"an":[70],"effective":[71],"intrusion":[72,153,162],"detection":[73,118,154,163],"method":[74,80],"address":[76],"these":[77],"requirements.":[78],"The":[79,93],"combines":[81],"refined":[82],"CAN":[83,100],"traffic":[84],"features":[85],"Deep":[89],"Learning":[90],"(DL)":[91],"network.":[92],"time":[94],"interval":[95],"series,":[96],"ID":[97],"message":[101],"payload":[102],"extracted":[104],"T-shaped":[107],"window,":[108],"vectorized":[109],"form":[111],"one-dimensional":[113],"data":[114],"frame,":[115],"completed":[120],"1D":[124],"deep":[125,151],"learning":[126],"model":[127],"using":[128],"efficient":[129],"convolution":[130],"calculation.":[131],"In":[132],"binary":[133],"multiclass":[135],"experiments":[136],"two":[138],"publicly":[139],"available":[140],"datasets,":[141],"feature":[143],"refinement":[144],"strategy":[145],"discussed.":[147],"Compared":[148],"existing":[150],"learning-based":[152],"methods,":[155],"our":[156],"approach":[157],"demonstrates":[158],"advantages":[159],"in":[160,172],"both":[161],"performance":[164],"computational":[166],"complexity,":[167],"highlighting":[168],"its":[169],"deployment":[170],"potential":[171],"embedded":[173],"IVN":[174],"environments.":[175]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":6}],"updated_date":"2025-12-21T23:12:01.093139","created_date":"2025-10-10T00:00:00"}