{"id":"https://openalex.org/W4388214733","doi":"https://doi.org/10.1109/fpl60245.2023.00040","title":"Exploring Highly Quantised Neural Networks for Intrusion Detection in Automotive CAN","display_name":"Exploring Highly Quantised Neural Networks for Intrusion Detection in Automotive CAN","publication_year":2023,"publication_date":"2023-09-04","ids":{"openalex":"https://openalex.org/W4388214733","doi":"https://doi.org/10.1109/fpl60245.2023.00040"},"language":"en","primary_location":{"id":"doi:10.1109/fpl60245.2023.00040","doi":"https://doi.org/10.1109/fpl60245.2023.00040","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fpl60245.2023.00040","pdf_url":null,"source":{"id":"https://openalex.org/S7407085929","display_name":"International Conference on Field Programmable Logic and Applications (FPL)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"2023 33rd International Conference on Field-Programmable Logic and Applications (FPL)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2401.11030","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5035589341","display_name":"Shashwat Khandelwal","orcid":"https://orcid.org/0000-0001-8838-0092","observed_orcids":["https://orcid.org/0000-0001-8838-0092"]},"institutions":[{"id":"https://openalex.org/I205274468","display_name":"Trinity College Dublin","ror":"https://ror.org/02tyrky19","country_code":"IE","type":"education","lineage":["https://openalex.org/I205274468"]}],"countries":["IE"],"is_corresponding":false,"raw_author_name":"Shashwat Khandelwal","raw_affiliation_strings":["Trinity College Dublin,Reconfigurable Computing Systems Lab, Electronic & Electrical Engineering,Ireland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Trinity College Dublin,Reconfigurable Computing Systems Lab, Electronic & Electrical Engineering,Ireland","institution_ids":["https://openalex.org/I205274468"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5065729450","display_name":"Shanker Shreejith","orcid":"https://orcid.org/0000-0002-9717-1804","observed_orcids":["https://orcid.org/0000-0002-9717-1804"]},"institutions":[{"id":"https://openalex.org/I205274468","display_name":"Trinity College Dublin","ror":"https://ror.org/02tyrky19","country_code":"IE","type":"education","lineage":["https://openalex.org/I205274468"]}],"countries":["IE"],"is_corresponding":false,"raw_author_name":"Shanker Shreejith","raw_affiliation_strings":["Trinity College Dublin,Reconfigurable Computing Systems Lab, Electronic & Electrical Engineering,Ireland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Trinity College Dublin,Reconfigurable Computing Systems Lab, Electronic & Electrical Engineering,Ireland","institution_ids":["https://openalex.org/I205274468"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I205274468"],"apc_list":null,"apc_paid":null,"fwci":2.4698,"has_fulltext":true,"cited_by_count":11,"citation_normalized_percentile":{"value":0.90176497,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"235","last_page":"241"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10400","display_name":"Network Security and Intrusion Detection","score":0.6845999956130981,"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":0.6845999956130981,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.10409999638795853,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.025800000876188278,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/intrusion-detection","display_name":"intrusion detection","score":0.9229999780654907},{"id":"https://openalex.org/keywords/multilayer-perceptron","display_name":"multilayer perceptron","score":0.8690000176429749},{"id":"https://openalex.org/keywords/denial-of-service-attacks","display_name":"denial-of-service attacks","score":0.8920000195503235},{"id":"https://openalex.org/keywords/spoofing-attacks","display_name":"spoofing attacks","score":0.8650000095367432},{"id":"https://openalex.org/keywords/fuzzing-attacks","display_name":"fuzzing attacks","score":0.9150000214576721},{"id":"https://openalex.org/keywords/hardware-acceleration","display_name":"hardware acceleration","score":0.3959999978542328},{"id":"https://openalex.org/keywords/finns","display_name":"Finns","score":0.5149999856948853},{"id":"https://openalex.org/keywords/brevitas","display_name":"brevitas","score":0.8970000147819519},{"id":"https://openalex.org/keywords/xilinx-fpga","display_name":"Xilinx FPGA","score":0.5839999914169312},{"id":"https://openalex.org/keywords/energy-efficient-inference","display_name":"energy-efficient inference","score":0.7379999756813049},{"id":"https://openalex.org/keywords/vehicle-network-security","display_name":"automotive network security","score":0.656000018119812},{"id":"https://openalex.org/keywords/electronic-control-units","display_name":"electronic control units","score":0.578000009059906}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7653101682662964},{"id":"https://openalex.org/C35525427","wikidata":"https://www.wikidata.org/wiki/Q745881","display_name":"Intrusion detection system","level":2,"score":0.6148127913475037},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5577071905136108},{"id":"https://openalex.org/C96324660","wikidata":"https://www.wikidata.org/wiki/Q205446","display_name":"Dataflow","level":2,"score":0.5406432151794434},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.49907922744750977},{"id":"https://openalex.org/C111065885","wikidata":"https://www.wikidata.org/wiki/Q1189053","display_name":"Fuzz testing","level":3,"score":0.4331757724285126},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.31371188163757324},{"id":"https://openalex.org/C2777904410","wikidata":"https://www.wikidata.org/wiki/Q7397","display_name":"Software","level":2,"score":0.10802733898162842},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/fpl60245.2023.00040","doi":"https://doi.org/10.1109/fpl60245.2023.00040","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fpl60245.2023.00040","pdf_url":null,"source":{"id":"https://openalex.org/S7407085929","display_name":"International Conference on Field Programmable Logic and Applications (FPL)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"2023 33rd International Conference on Field-Programmable Logic and Applications (FPL)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2401.11030","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2401.11030","pdf_url":"https://arxiv.org/pdf/2401.11030","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:oai:www.tara.tcd.ie:2262/102968","doi":null,"is_oa":true,"landing_page_url":"http://hdl.handle.net/2262/102968","pdf_url":"https://www.tara.tcd.ie/bitstream/2262/102968/1/8309-FPL-23.pdf","source":{"id":"https://openalex.org/S4306400387","display_name":"Trinity's Access to Research Output (TARA) (Trinity College Dublin)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205274468","host_organization_name":"Trinity College Dublin","host_organization_lineage":["https://openalex.org/I205274468"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-sa","license_id":"https://openalex.org/licenses/cc-by-nc-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Conference Paper"},{"id":"doi:10.48550/arxiv.2401.11030","doi":"https://doi.org/10.48550/arxiv.2401.11030","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2401.11030","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article-journal"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2401.11030","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2401.11030","pdf_url":"https://arxiv.org/pdf/2401.11030","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.4943999946117401}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4388214733.pdf","grobid_xml":"https://content.openalex.org/works/W4388214733.grobid-xml"},"referenced_works_count":36,"referenced_works":["https://openalex.org/W2106958114","https://openalex.org/W2116520617","https://openalex.org/W2207491215","https://openalex.org/W2276974676","https://openalex.org/W2286343943","https://openalex.org/W2399576380","https://openalex.org/W2550909413","https://openalex.org/W2565125333","https://openalex.org/W2593900992","https://openalex.org/W2620604911","https://openalex.org/W2772709151","https://openalex.org/W2787513570","https://openalex.org/W2787771094","https://openalex.org/W2792310067","https://openalex.org/W2888088168","https://openalex.org/W2898623510","https://openalex.org/W2921212998","https://openalex.org/W2959120033","https://openalex.org/W2979202956","https://openalex.org/W2981094074","https://openalex.org/W3014212182","https://openalex.org/W3128550955","https://openalex.org/W3164321464","https://openalex.org/W4205625641","https://openalex.org/W4210302132","https://openalex.org/W4210842733","https://openalex.org/W4220775946","https://openalex.org/W4293055909","https://openalex.org/W4294860585","https://openalex.org/W4311839761","https://openalex.org/W4312762561","https://openalex.org/W4321637061","https://openalex.org/W4366377785","https://openalex.org/W4378976213","https://openalex.org/W4379116112","https://openalex.org/W6950685506"],"related_works":["https://openalex.org/W4321637061","https://openalex.org/W7214885054","https://openalex.org/W7140169664","https://openalex.org/W4385756467","https://openalex.org/W6999715483","https://openalex.org/W2913547808","https://openalex.org/W2993447978","https://openalex.org/W4404953642","https://openalex.org/W4407690660","https://openalex.org/W4412787113"],"abstract_inverted_index":{"Vehicles":[0],"today":[1],"comprise":[2],"intelligent":[3],"systems":[4,13,60],"like":[5],"connected":[6],"autonomous":[7],"driving":[8,11,18],"and":[9,28,54,61,113,151,170,181,217],"advanced":[10],"assistance":[12],"(ADAS)":[14],"to":[15,26,76,102],"enhance":[16],"the":[17,37,42,64,109,139,167,171,189,201,208,230,234,237],"experience,":[19],"which":[20],"is":[21,177],"enabled":[22,89],"through":[23,90,156,188],"increased":[24],"connectivity":[25,39],"infrastructure":[27],"fusion":[29],"of":[30,66,134,142,166,225,248],"information":[31],"from":[32,138,192,251],"different":[33],"sensing":[34],"modes.":[35],"However,":[36],"rising":[38],"coupled":[40],"with":[41,220,229,264],"legacy":[43],"network":[44,145],"architecture":[45,153],"within":[46],"vehicles":[47],"can":[48,210,240],"be":[49],"exploited":[50],"for":[51,123,262],"launching":[52],"active":[53],"passive":[55],"attacks":[56,137],"on":[57,227],"critical":[58,268],"vehicle":[59],"directly":[62],"affecting":[63],"safety":[65],"passengers.":[67],"Machine":[68],"learning-based":[69],"intrusion":[70],"detection":[71,244],"models":[72,98],"have":[73],"been":[74],"shown":[75],"successfully":[77],"detect":[78,104,211],"multiple":[79,105,136],"targeted":[80],"attack":[81,106,213],"vectors":[82],"in":[83,163,233,267],"recent":[84],"literature,":[85],"whose":[86],"deployments":[87],"are":[88,99,154],"quantised":[91],"neural":[92,152],"networks":[93],"targeting":[94,194],"low-power":[95],"platforms.":[96],"Multiple":[97],"often":[100],"required":[101],"simultaneously":[103],"vectors,":[107],"increasing":[108],"area,":[110],"(resource)":[111],"cost,":[112],"energy":[114],"consumption.":[115],"In":[116],"this":[117],"paper,":[118],"we":[119],"present":[120],"a":[121,129,157,184,221,246],"case":[122],"utilising":[124],"custom-quantised":[125],"MLP's":[126],"(CQMLP)":[127],"as":[128,183,207],"multi-class":[130],"classification":[131],"model,":[132,204],"capable":[133],"detecting":[135],"benign":[140],"flow":[141],"controller":[143],"area":[144],"(CAN)":[146],"messages.":[147],"The":[148],"specific":[149],"quantisation":[150],"determined":[155],"joint":[158],"design":[159],"space":[160],"exploration,":[161],"resulting":[162],"our":[164],"choice":[165],"2-bit":[168,175,202],"precision":[169],"n-layer":[172],"MLP.":[173],"Our":[174],"version":[176],"trained":[178],"using":[179],"Brevitas":[180],"optimised":[182],"dataflow":[185,238],"hardware":[186],"model":[187,239],"FINN":[190],"toolflow":[191],"AMD/Xilinx,":[193],"an":[195,265],"XCZU7EV":[196],"device.":[197],"We":[198],"show":[199],"that":[200],"CQMLP":[203],"when":[205],"integrated":[206],"IDS,":[209],"malicious":[212],"messages":[214],"(DoS,":[215],"fuzzing,":[216],"spoofing":[218],"attack)":[219],"very":[222],"high":[223],"accuracy":[224],"99.9%,":[226],"par":[228],"state-of-the-art":[231],"methods":[232],"literature.":[235],"Furthermore,":[236],"perform":[241],"line":[242],"rate":[243],"at":[245],"latency":[247],"0.11":[249],"ms":[250],"message":[252],"reception":[253],"while":[254],"consuming":[255],"0.23":[256],"mJ/inference,":[257],"making":[258],"it":[259],"ideally":[260],"suited":[261],"integration":[263],"ECU":[266],"CAN":[269],"networks.":[270]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":8}],"updated_date":"2026-11-10T11:39:43.021295","created_date":"2023-11-03T00:00:00"}