{"id":"https://openalex.org/W4411949847","doi":"https://doi.org/10.1109/vnc64509.2025.11054177","title":"Real-Time Evasion Detection in Tree Ensemble Automotive Intrusion Detection Systems","display_name":"Real-Time Evasion Detection in Tree Ensemble Automotive Intrusion Detection Systems","publication_year":2025,"publication_date":"2025-06-02","ids":{"openalex":"https://openalex.org/W4411949847","doi":"https://doi.org/10.1109/vnc64509.2025.11054177"},"language":"en","primary_location":{"id":"doi:10.1109/vnc64509.2025.11054177","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vnc64509.2025.11054177","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE Vehicular Networking Conference (VNC)","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/A5116057678","display_name":"Valency Oscar Colaco","orcid":"https://orcid.org/0000-0001-6405-4794"},"institutions":[{"id":"https://openalex.org/I102134673","display_name":"Link\u00f6ping University","ror":"https://ror.org/05ynxx418","country_code":"SE","type":"education","lineage":["https://openalex.org/I102134673"]}],"countries":["SE"],"is_corresponding":true,"raw_author_name":"Valency Oscar Colaco","raw_affiliation_strings":["Link&#x00F6;ping University,Sweden"],"affiliations":[{"raw_affiliation_string":"Link&#x00F6;ping University,Sweden","institution_ids":["https://openalex.org/I102134673"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5068181564","display_name":"Simin Nadjm\u2010Tehrani","orcid":"https://orcid.org/0000-0002-1485-0802"},"institutions":[{"id":"https://openalex.org/I102134673","display_name":"Link\u00f6ping University","ror":"https://ror.org/05ynxx418","country_code":"SE","type":"education","lineage":["https://openalex.org/I102134673"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Simin Nadjm-Tehrani","raw_affiliation_strings":["Link&#x00F6;ping University,Sweden"],"affiliations":[{"raw_affiliation_string":"Link&#x00F6;ping University,Sweden","institution_ids":["https://openalex.org/I102134673"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5116057678"],"corresponding_institution_ids":["https://openalex.org/I102134673"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.19575763,"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":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9818000197410583,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive 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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9818000197410583,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive 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":0.9763000011444092,"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/T12597","display_name":"Fire Detection and Safety Systems","score":0.9502000212669373,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"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":0.8529395461082458},{"id":"https://openalex.org/keywords/automotive-industry","display_name":"Automotive industry","score":0.6632089614868164},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6585845351219177},{"id":"https://openalex.org/keywords/evasion","display_name":"Evasion (ethics)","score":0.64469313621521},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.48883336782455444},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.4013754725456238},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3695923388004303},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1514066457748413},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.14755108952522278}],"concepts":[{"id":"https://openalex.org/C35525427","wikidata":"https://www.wikidata.org/wiki/Q745881","display_name":"Intrusion detection system","level":2,"score":0.8529395461082458},{"id":"https://openalex.org/C526921623","wikidata":"https://www.wikidata.org/wiki/Q190117","display_name":"Automotive industry","level":2,"score":0.6632089614868164},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6585845351219177},{"id":"https://openalex.org/C2781251061","wikidata":"https://www.wikidata.org/wiki/Q5416089","display_name":"Evasion (ethics)","level":3,"score":0.64469313621521},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.48883336782455444},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.4013754725456238},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3695923388004303},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1514066457748413},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.14755108952522278},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","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/C203014093","wikidata":"https://www.wikidata.org/wiki/Q101929","display_name":"Immunology","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C8891405","wikidata":"https://www.wikidata.org/wiki/Q1059","display_name":"Immune system","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/vnc64509.2025.11054177","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vnc64509.2025.11054177","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE Vehicular Networking Conference (VNC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W1564743226","https://openalex.org/W2101234009","https://openalex.org/W2153861733","https://openalex.org/W2295598076","https://openalex.org/W2803414046","https://openalex.org/W2962061624","https://openalex.org/W2963197901","https://openalex.org/W2963952467","https://openalex.org/W2971223760","https://openalex.org/W2985386758","https://openalex.org/W3019040220","https://openalex.org/W3164936723","https://openalex.org/W3168146198","https://openalex.org/W4288061065","https://openalex.org/W4317659178","https://openalex.org/W4323966580","https://openalex.org/W4327652204","https://openalex.org/W4378976778","https://openalex.org/W4381326122","https://openalex.org/W4386811896","https://openalex.org/W4394862918","https://openalex.org/W4396707597","https://openalex.org/W4398151316","https://openalex.org/W4407272590","https://openalex.org/W6743618022","https://openalex.org/W6787727387","https://openalex.org/W7065792006"],"related_works":["https://openalex.org/W4382644535","https://openalex.org/W2373230814","https://openalex.org/W2522768275","https://openalex.org/W2352938035","https://openalex.org/W2351672553","https://openalex.org/W2373392303","https://openalex.org/W2765894405","https://openalex.org/W1884735063","https://openalex.org/W2808001300","https://openalex.org/W1548771250"],"abstract_inverted_index":{"Safety-critical":[0],"functions":[1],"in":[2,69,154,183],"modern":[3],"vehicles":[4],"rely":[5],"on":[6],"electronic":[7],"control":[8],"units":[9],"that":[10,56,78,143,187],"communicate":[11],"using":[12,175],"the":[13,84,116,125,149,200,206,211,215,239,250,255,273,285],"controller":[14],"area":[15],"network":[16],"(CAN)":[17],"protocol,":[18],"which":[19,151],"lacks":[20],"vital":[21],"security":[22,212],"features.":[23],"In":[24],"this":[25,62,64],"context,":[26],"machine":[27],"learning":[28],"(ML)":[29],"based":[30],"intrusion":[31],"detection":[32,157,185,245],"systems":[33],"(IDSs)":[34],"were":[35],"proposed":[36],"as":[37,97,107],"a":[38,76,98,184,229],"solution":[39],"to":[40,148,205],"improve":[41],"cyber":[42],"resilience":[43],"through":[44],"real-time":[45,291],"attack":[46,202,252],"detection.":[47],"However,":[48],"these":[49],"ML-IDSs":[50],"must":[51],"also":[52,141],"withstand":[53],"evasion":[54,80,109,219,256],"attacks":[55,68],"could":[57],"compromise":[58],"vehicular":[59],"safety.":[60],"To":[61],"end,":[63],"paper":[65],"addresses":[66],"such":[67],"misuse-based":[70],"tree":[71,118,138],"ensemble":[72,119],"IDSs":[73],"and":[74,133,265,280],"proposes":[75],"method":[77,195],"detects":[79],"attempts.":[81,110,220],"It":[82],"uses":[83],"ordered":[85],"set":[86],"of":[87,124,156,170,190,214,254,261],"reached":[88],"leaf":[89],"nodes":[90],"activated":[91],"by":[92,159],"correctly":[93],"classified":[94],"training":[95],"samples":[96,257],"normality":[99],"baseline.":[100],"An":[101],"autoencoder-based":[102,168],"detector":[103],"then":[104],"identifies":[105],"deviations":[106],"likely":[108],"Our":[111],"approach":[112],"does":[113],"not":[114],"modify":[115],"protected":[117],"IDS,":[120],"assumes":[121],"no":[122],"knowledge":[123],"process":[126,186],"for":[127,277,287],"generating":[128],"adversarial":[129,207],"examples":[130],"(ensuring":[131],"generalisability),":[132],"works":[134],"with":[135,166,258],"any":[136],"additive":[137],"ensemble.":[139],"We":[140],"prove":[142],"it":[144,235],"is":[145,188],"mathematically":[146],"equivalent":[147],"state-of-the-art,":[150],"we":[152,224],"advance":[153],"terms":[155],"speed":[158],"replacing":[160],"its":[161],"Hamming":[162],"distance-based":[163],"deviation":[164],"search":[165],"an":[167,259,267,288],"model":[169],"typical":[171],"predictive":[172],"behavior":[173],"trained":[174],"our":[176,194],"custom":[177],"loss":[178],"function.":[179],"This":[180],"enhancement":[181],"results":[182],"orders":[189],"magnitude":[191],"faster.":[192],"Additionally,":[193],"offers":[196],"nuanced":[197],"insights":[198],"regarding":[199],"pre-evasion":[201,251],"signature":[203],"prior":[204],"perturbation,":[208],"thereby":[209,283],"enriching":[210],"analysis":[213],"features":[216],"targeted":[217],"during":[218],"The":[221],"prototype":[222],"system":[223],"present,":[225],"called":[226],"Maverick,":[227],"has":[228,266],"very":[230],"low":[231],"prediction":[232,269],"latency,":[233],"making":[234],"85-563x":[236],"faster":[237],"than":[238,263],"current":[240],"state-of-the-art":[241],"while":[242],"maintaining":[243],"identical":[244],"accuracy.":[246],"Finally,":[247],"Maverick":[248],"predicts":[249],"signatures":[253],"accuracy":[260],"more":[262],"93%":[264],"average":[268],"time":[270],"well":[271],"below":[272],"message":[274],"transmission":[275],"rate":[276],"CAN":[278,281],"2.0":[279],"FD,":[282],"satisfying":[284],"criteria":[286],"evasion-hardened":[289],"&":[290],"automotive":[292],"IDS.":[293]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
