{"id":"https://openalex.org/W4293057825","doi":"https://doi.org/10.1109/vtc2022-spring54318.2022.9860358","title":"CANLite: Anomaly Detection in Controller Area Networks with Multitask Learning","display_name":"CANLite: Anomaly Detection in Controller Area Networks with Multitask Learning","publication_year":2022,"publication_date":"2022-06-01","ids":{"openalex":"https://openalex.org/W4293057825","doi":"https://doi.org/10.1109/vtc2022-spring54318.2022.9860358"},"language":"en","primary_location":{"id":"doi:10.1109/vtc2022-spring54318.2022.9860358","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vtc2022-spring54318.2022.9860358","pdf_url":null,"source":{"id":"https://openalex.org/S4363607744","display_name":"2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring)","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/A5112695907","display_name":"Prashanth Balaji","orcid":null},"institutions":[{"id":"https://openalex.org/I168635309","display_name":"University of Calgary","ror":"https://ror.org/03yjb2x39","country_code":"CA","type":"education","lineage":["https://openalex.org/I168635309"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Prashanth Balaji","raw_affiliation_strings":["University of Calgary"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Calgary","institution_ids":["https://openalex.org/I168635309"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081502954","display_name":"Majid Ghaderi","orcid":"https://orcid.org/0000-0002-3783-4346"},"institutions":[{"id":"https://openalex.org/I168635309","display_name":"University of Calgary","ror":"https://ror.org/03yjb2x39","country_code":"CA","type":"education","lineage":["https://openalex.org/I168635309"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Majid Ghaderi","raw_affiliation_strings":["University of Calgary"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Calgary","institution_ids":["https://openalex.org/I168635309"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100670033","display_name":"Hongwen Zhang","orcid":"https://orcid.org/0000-0003-2702-5956"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hongwen Zhang","raw_affiliation_strings":["Wedge Networks"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Wedge Networks","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.8705,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.926256,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10761","display_name":"Vehicular Ad Hoc Networks (VANETs)","score":0.9990000128746033,"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":0.9990000128746033,"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/T10800","display_name":"Forensic Toxicology and Drug Analysis","score":0.9846000075340271,"subfield":{"id":"https://openalex.org/subfields/3005","display_name":"Toxicology"},"field":{"id":"https://openalex.org/fields/30","display_name":"Pharmacology, Toxicology and Pharmaceutics"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.975600004196167,"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":0.7939367294311523},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.7251719832420349},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.7239226698875427},{"id":"https://openalex.org/keywords/memory-footprint","display_name":"Memory footprint","score":0.71717768907547},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.6791207194328308},{"id":"https://openalex.org/keywords/footprint","display_name":"Footprint","score":0.6458060145378113},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.49899768829345703},{"id":"https://openalex.org/keywords/can-bus","display_name":"CAN bus","score":0.49625569581985474},{"id":"https://openalex.org/keywords/authentication","display_name":"Authentication (law)","score":0.477383553981781},{"id":"https://openalex.org/keywords/controller","display_name":"Controller (irrigation)","score":0.4741848409175873},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.47158730030059814},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4448348879814148},{"id":"https://openalex.org/keywords/embedded-system","display_name":"Embedded system","score":0.38437673449516296},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.32617902755737305},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.29385924339294434},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.24644815921783447}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7939367294311523},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.7251719832420349},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.7239226698875427},{"id":"https://openalex.org/C74912251","wikidata":"https://www.wikidata.org/wiki/Q6815727","display_name":"Memory footprint","level":2,"score":0.71717768907547},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.6791207194328308},{"id":"https://openalex.org/C132943942","wikidata":"https://www.wikidata.org/wiki/Q2562511","display_name":"Footprint","level":2,"score":0.6458060145378113},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.49899768829345703},{"id":"https://openalex.org/C201762086","wikidata":"https://www.wikidata.org/wiki/Q728183","display_name":"CAN bus","level":2,"score":0.49625569581985474},{"id":"https://openalex.org/C148417208","wikidata":"https://www.wikidata.org/wiki/Q4825882","display_name":"Authentication (law)","level":2,"score":0.477383553981781},{"id":"https://openalex.org/C203479927","wikidata":"https://www.wikidata.org/wiki/Q5165939","display_name":"Controller (irrigation)","level":2,"score":0.4741848409175873},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.47158730030059814},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4448348879814148},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.38437673449516296},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.32617902755737305},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.29385924339294434},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.24644815921783447},{"id":"https://openalex.org/C111368507","wikidata":"https://www.wikidata.org/wiki/Q43518","display_name":"Oceanography","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C6557445","wikidata":"https://www.wikidata.org/wiki/Q173113","display_name":"Agronomy","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/vtc2022-spring54318.2022.9860358","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vtc2022-spring54318.2022.9860358","pdf_url":null,"source":{"id":"https://openalex.org/S4363607744","display_name":"2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.49000000953674316,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320325651","display_name":"Alberta Innovates","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W87635799","https://openalex.org/W1522301498","https://openalex.org/W2064675550","https://openalex.org/W2101234009","https://openalex.org/W2133854595","https://openalex.org/W2517554157","https://openalex.org/W2561208905","https://openalex.org/W2624871570","https://openalex.org/W2742079690","https://openalex.org/W2925076195","https://openalex.org/W2948384692","https://openalex.org/W2959120033","https://openalex.org/W3003889215","https://openalex.org/W3014212182","https://openalex.org/W3089775054","https://openalex.org/W3092232275","https://openalex.org/W3092255243","https://openalex.org/W3105105644","https://openalex.org/W3206867092","https://openalex.org/W4295312788","https://openalex.org/W6603557143","https://openalex.org/W6739365718","https://openalex.org/W6742058293","https://openalex.org/W6766978945"],"related_works":["https://openalex.org/W2383111961","https://openalex.org/W2365952365","https://openalex.org/W2352448290","https://openalex.org/W2380820513","https://openalex.org/W2913146933","https://openalex.org/W2372385138","https://openalex.org/W4296359239","https://openalex.org/W2101155126","https://openalex.org/W3137434606","https://openalex.org/W4372263373"],"abstract_inverted_index":{"The":[0],"Controller":[1],"Area":[2],"Network":[3],"(CAN)":[4],"bus":[5,101],"has":[6],"been":[7],"a":[8,25,96,108,131,157],"widely":[9],"implemented":[10],"standard":[11],"for":[12],"in-vehicle":[13],"communication":[14],"between":[15],"vehicle":[16],"subsystems.":[17],"However,":[18],"since":[19],"CAN":[20,38],"was":[21],"never":[22],"designed":[23],"with":[24],"focus":[26,56,69],"on":[27,57,70,99,111],"security,":[28],"attackers":[29],"can":[30],"exploit":[31],"the":[32,49,61,65,100,112,117,147,167,175,182],"lack":[33],"of":[34,64,120,178],"message":[35],"authentication":[36],"in":[37,54,60],"to":[39,44,76,86,116,139],"inject":[40],"crafted":[41],"malicious":[42],"payloads":[43],"disable":[45],"critical":[46],"systems":[47],"onboard":[48],"vehicle.":[50],"While":[51],"previous":[52],"works":[53],"literature":[55],"detecting":[58],"deviations":[59,143],"normal":[62],"behavior":[63,92],"bus,":[66],"they":[67,74],"merely":[68],"individual":[71,84],"sensors.":[72],"Hence":[73],"fail":[75],"identify":[77],"stealthy":[78],"attacks":[79],"that":[80,164],"do":[81],"not":[82],"cause":[83],"sensors":[85],"deviate":[87],"substantially":[88],"from":[89],"their":[90],"expected":[91],"but":[93],"still":[94,173],"have":[95],"significant":[97],"impact":[98],"state.":[102],"Further,":[103],"such":[104,141],"approaches":[105],"often":[106],"impose":[107],"computational":[109],"strain":[110],"deployed":[113],"system":[114,135],"due":[115],"high":[118],"magnitude":[119],"consumed":[121],"resources":[122],"at":[123],"run-time.":[124],"To":[125],"this":[126],"end,":[127],"we":[128],"propose":[129],"CANLite,":[130],"lightweight":[132],"anomaly":[133],"detection":[134,179],"utilizing":[136],"multitask":[137],"learning":[138],"detect":[140],"subtle":[142],"while":[144,172],"significantly":[145],"reducing":[146],"memory":[148,168],"footprint.":[149],"We":[150],"trained":[151],"and":[152],"evaluated":[153],"our":[154],"model":[155],"against":[156],"state-of-the-art":[158],"baseline":[159],"approach.":[160],"Our":[161],"results":[162],"indicate":[163],"CANLite":[165],"reduces":[166],"footprint":[169],"by":[170],"50%":[171],"achieving":[174],"same":[176],"level":[177],"performance":[180],"as":[181],"baseline.":[183]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":3}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}