{"id":"https://openalex.org/W4413755492","doi":"https://doi.org/10.1109/isvlsi65124.2025.11130204","title":"A Multi-Scale Lightweight 1D-CNN for Efficient CAN Intrusion Detection","display_name":"A Multi-Scale Lightweight 1D-CNN for Efficient CAN Intrusion Detection","publication_year":2025,"publication_date":"2025-07-06","ids":{"openalex":"https://openalex.org/W4413755492","doi":"https://doi.org/10.1109/isvlsi65124.2025.11130204"},"language":"en","primary_location":{"id":"doi:10.1109/isvlsi65124.2025.11130204","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isvlsi65124.2025.11130204","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE Computer Society Annual Symposium on VLSI (ISVLSI)","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/A5020376195","display_name":"Xuke Yan","orcid":"https://orcid.org/0000-0002-1765-257X"},"institutions":[{"id":"https://openalex.org/I177721651","display_name":"Oakland University","ror":"https://ror.org/01ythxj32","country_code":"US","type":"education","lineage":["https://openalex.org/I177721651"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Xuke Yan","raw_affiliation_strings":["Oakland University,Department of Computer Science and Engineering,USA"],"affiliations":[{"raw_affiliation_string":"Oakland University,Department of Computer Science and Engineering,USA","institution_ids":["https://openalex.org/I177721651"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5021600387","display_name":"Linxi Zhang","orcid":"https://orcid.org/0000-0001-7371-8695"},"institutions":[{"id":"https://openalex.org/I1629065","display_name":"Central Michigan University","ror":"https://ror.org/02xawj266","country_code":"US","type":"education","lineage":["https://openalex.org/I1629065"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Linxi Zhang","raw_affiliation_strings":["Central Michigan University,Department of Computer Science,USA"],"affiliations":[{"raw_affiliation_string":"Central Michigan University,Department of Computer Science,USA","institution_ids":["https://openalex.org/I1629065"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5020376195"],"corresponding_institution_ids":["https://openalex.org/I177721651"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.11367266,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10069","display_name":"Antenna Design and Analysis","score":0.9448000192642212,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace 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/T10069","display_name":"Antenna Design and Analysis","score":0.9448000192642212,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace 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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9325000047683716,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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":0.9251000285148621,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7124334573745728},{"id":"https://openalex.org/keywords/intrusion-detection-system","display_name":"Intrusion detection system","score":0.6592363119125366},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.5483739972114563},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3589986562728882},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.06065651774406433}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7124334573745728},{"id":"https://openalex.org/C35525427","wikidata":"https://www.wikidata.org/wiki/Q745881","display_name":"Intrusion detection system","level":2,"score":0.6592363119125366},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.5483739972114563},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3589986562728882},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.06065651774406433},{"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/isvlsi65124.2025.11130204","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isvlsi65124.2025.11130204","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE Computer Society Annual Symposium on VLSI (ISVLSI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Climate action","score":0.47999998927116394,"id":"https://metadata.un.org/sdg/13"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W2531409750","https://openalex.org/W2752782242","https://openalex.org/W2922509574","https://openalex.org/W2979202956","https://openalex.org/W3034421924","https://openalex.org/W3092232275","https://openalex.org/W3124913209","https://openalex.org/W4206382210","https://openalex.org/W4210403898","https://openalex.org/W4220686149","https://openalex.org/W4289752563","https://openalex.org/W4296501357","https://openalex.org/W4302424538","https://openalex.org/W4308605894","https://openalex.org/W4378976213","https://openalex.org/W4391927311","https://openalex.org/W4401508383","https://openalex.org/W4410969219"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052"],"abstract_inverted_index":{"The":[0,170],"Controller":[1],"Area":[2],"Network":[3],"(CAN)":[4],"bus":[5],"is":[6],"widely":[7],"used":[8],"in":[9],"modern":[10],"vehicles,":[11],"but":[12],"remains":[13],"vulnerable":[14],"to":[15,19,35,54,204],"various":[16],"cyberattacks":[17],"due":[18],"its":[20],"lack":[21],"of":[22,119,212],"built-in":[23],"security":[24],"mechanisms.":[25],"Machine":[26],"learning-based":[27],"intrusion":[28,78],"detection":[29,205],"systems":[30],"(IDSs)":[31],"have":[32],"been":[33],"proposed":[34,160],"detect":[36],"anomalies.":[37],"However,":[38],"many":[39],"existing":[40],"approaches":[41],"rely":[42],"on":[43,56,154],"deep":[44],"or":[45,132],"recurrent":[46],"architectures":[47],"that":[48,85,147,195],"are":[49],"computationally":[50,134],"intensive":[51],"and":[52,91,104,186,200,210],"difficult":[53],"deploy":[55],"resource-constrained":[57],"automotive":[58,216],"environments.":[59,217],"To":[60,122],"this":[61],"end,":[62],"we":[63,83,138],"propose":[64],"a":[65,142],"multi-scale":[66,198],"lightweight":[67],"one-dimensional":[68],"convolutional":[69],"neural":[70],"network":[71],"(1D-CNN)":[72],"designed":[73],"specifically":[74],"for":[75,215],"efficient":[76],"CAN":[77,164],"detection.":[79],"Unlike":[80],"previous":[81],"studies,":[82],"consider":[84],"realistic":[86],"attacks":[87],"can":[88],"be":[89],"complex":[90],"may":[92],"not":[93],"exhibit":[94],"regular":[95],"patterns,":[96],"often":[97],"displaying":[98],"diverse":[99],"temporal":[100],"behaviors.":[101],"We":[102,157],"design":[103,199],"employ":[105],"multiple":[106],"depthwise":[107],"separable":[108],"Conv1D":[109],"branches":[110],"with":[111,181],"varying":[112],"kernel":[113],"sizes,":[114],"enabling":[115],"more":[116],"effective":[117],"handling":[118],"such":[120],"attacks.":[121],"further":[123,193],"improve":[124],"the":[125,155,159,197,208,213],"performance,":[126,206],"rather":[127],"than":[128],"concatenating":[129],"feature":[130],"maps":[131],"using":[133,162],"expensive":[135],"fusion":[136],"modules,":[137],"introduce":[139],"scale":[140,201],"gate,":[141],"parameter-free":[143],"softmax":[144],"gating":[145],"mechanism":[146],"adaptively":[148],"re-weights":[149],"each":[150],"branch\u2019s":[151],"output":[152],"based":[153],"input.":[156],"evaluate":[158],"IDS":[161],"real":[163],"traffic":[165],"collected":[166],"from":[167],"different":[168],"cars.":[169],"model":[171],"achieves":[172],"satisfying":[173],"accuracy":[174],"under":[175],"our":[176],"most":[177],"challenging":[178],"attack":[179],"strategy":[180],"approximately":[182],"30":[183],"K":[184],"parameters":[185],"2.7":[187],"M":[188],"FLOPs.":[189],"Our":[190],"ablation":[191],"studies":[192],"confirm":[194],"both":[196],"gate":[202],"contribute":[203],"demonstrating":[207],"effectiveness":[209],"efficiency":[211],"approach":[214]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}