{"id":"https://openalex.org/W4414647386","doi":"https://doi.org/10.1109/vtc2025-spring65109.2025.11174733","title":"Adversarial Attacks on Machine Learning-Based IDS for V2X Networks: A CICIoV2024 Study","display_name":"Adversarial Attacks on Machine Learning-Based IDS for V2X Networks: A CICIoV2024 Study","publication_year":2025,"publication_date":"2025-06-17","ids":{"openalex":"https://openalex.org/W4414647386","doi":"https://doi.org/10.1109/vtc2025-spring65109.2025.11174733"},"language":"en","primary_location":{"id":"doi:10.1109/vtc2025-spring65109.2025.11174733","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vtc2025-spring65109.2025.11174733","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 101st Vehicular Technology Conference (VTC2025-Spring)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5016319775","display_name":"Quazi Mamun","orcid":"https://orcid.org/0000-0003-2196-7651"},"institutions":[{"id":"https://openalex.org/I153230381","display_name":"Charles Sturt University","ror":"https://ror.org/00wfvh315","country_code":"AU","type":"education","lineage":["https://openalex.org/I153230381"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Quazi Mamun","raw_affiliation_strings":["School of Comp, Maths & Engg, Charles Sturt University,NSW,Australia"],"raw_orcid":"https://orcid.org/0000-0003-2196-7651","affiliations":[{"raw_affiliation_string":"School of Comp, Maths & Engg, Charles Sturt University,NSW,Australia","institution_ids":["https://openalex.org/I153230381"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113698509","display_name":"Md. Mehedi Hasan","orcid":"https://orcid.org/0000-0002-8826-1576"},"institutions":[{"id":"https://openalex.org/I153230381","display_name":"Charles Sturt University","ror":"https://ror.org/00wfvh315","country_code":"AU","type":"education","lineage":["https://openalex.org/I153230381"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Md Mehedi Hasan","raw_affiliation_strings":["School of Comp, Maths & Engg, Charles Sturt University,NSW,Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Comp, Maths & Engg, Charles Sturt University,NSW,Australia","institution_ids":["https://openalex.org/I153230381"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006124696","display_name":"Tu Dac Ho","orcid":"https://orcid.org/0000-0001-7215-0479"},"institutions":[{"id":"https://openalex.org/I204778367","display_name":"Norwegian University of Science and Technology","ror":"https://ror.org/05xg72x27","country_code":"NO","type":"education","lineage":["https://openalex.org/I204778367"]}],"countries":["NO"],"is_corresponding":false,"raw_author_name":"Tu Dac Ho","raw_affiliation_strings":["The Norwegian University of Science and Technology,Dept of Info Security and Comm Tech,Trondheim,Norway"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Norwegian University of Science and Technology,Dept of Info Security and Comm Tech,Trondheim,Norway","institution_ids":["https://openalex.org/I204778367"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091321696","display_name":"Zhenni Pan","orcid":"https://orcid.org/0000-0002-5332-6923"},"institutions":[{"id":"https://openalex.org/I150744194","display_name":"Waseda University","ror":"https://ror.org/00ntfnx83","country_code":"JP","type":"education","lineage":["https://openalex.org/I150744194"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Zhenni Pan","raw_affiliation_strings":["Waseda University,Faculty of Science and Engineering,Tokyo,Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Waseda University,Faculty of Science and Engineering,Tokyo,Japan","institution_ids":["https://openalex.org/I150744194"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5025087534","display_name":"Shigeru Shimamoto","orcid":"https://orcid.org/0000-0002-2266-7362"},"institutions":[{"id":"https://openalex.org/I150744194","display_name":"Waseda University","ror":"https://ror.org/00ntfnx83","country_code":"JP","type":"education","lineage":["https://openalex.org/I150744194"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Shigeru Shimamoto","raw_affiliation_strings":["Waseda University,Faculty of Science and Technology,Tokyo,Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Waseda University,Faculty of Science and Technology,Tokyo,Japan","institution_ids":["https://openalex.org/I150744194"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.31135545,"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/T10400","display_name":"Network Security and Intrusion Detection","score":0.9907000064849854,"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":0.9907000064849854,"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/T10917","display_name":"Smart Grid Security and Resilience","score":0.9717000126838684,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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":0.9415000081062317,"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/adversarial-system","display_name":"Adversarial system","score":0.8910999894142151},{"id":"https://openalex.org/keywords/intrusion-detection-system","display_name":"Intrusion detection system","score":0.5467000007629395},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5019999742507935},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.4740999937057495},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4383000135421753},{"id":"https://openalex.org/keywords/intrusion","display_name":"Intrusion","score":0.42879998683929443},{"id":"https://openalex.org/keywords/gradient-descent","display_name":"Gradient descent","score":0.39809998869895935},{"id":"https://openalex.org/keywords/adversarial-machine-learning","display_name":"Adversarial machine learning","score":0.374099999666214}],"concepts":[{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.8910999894142151},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7856000065803528},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.6700000166893005},{"id":"https://openalex.org/C35525427","wikidata":"https://www.wikidata.org/wiki/Q745881","display_name":"Intrusion detection system","level":2,"score":0.5467000007629395},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5019999742507935},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.4740999937057495},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.43869999051094055},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4383000135421753},{"id":"https://openalex.org/C158251709","wikidata":"https://www.wikidata.org/wiki/Q354025","display_name":"Intrusion","level":2,"score":0.42879998683929443},{"id":"https://openalex.org/C153258448","wikidata":"https://www.wikidata.org/wiki/Q1199743","display_name":"Gradient descent","level":3,"score":0.39809998869895935},{"id":"https://openalex.org/C2778403875","wikidata":"https://www.wikidata.org/wiki/Q20312394","display_name":"Adversarial machine learning","level":3,"score":0.374099999666214},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.35199999809265137},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.34790000319480896},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.31779998540878296},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.31310001015663147},{"id":"https://openalex.org/C27061796","wikidata":"https://www.wikidata.org/wiki/Q745881","display_name":"Intrusion prevention system","level":3,"score":0.305400013923645},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2996000051498413},{"id":"https://openalex.org/C541664917","wikidata":"https://www.wikidata.org/wiki/Q14001","display_name":"Malware","level":2,"score":0.28780001401901245},{"id":"https://openalex.org/C2780771206","wikidata":"https://www.wikidata.org/wiki/Q3271761","display_name":"Safeguard","level":2,"score":0.26249998807907104},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.258899986743927}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/vtc2025-spring65109.2025.11174733","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vtc2025-spring65109.2025.11174733","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 101st Vehicular Technology Conference (VTC2025-Spring)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":3,"referenced_works":["https://openalex.org/W2603766943","https://openalex.org/W3103836116","https://openalex.org/W4319163914"],"related_works":[],"abstract_inverted_index":{"With":[0],"the":[1,64,73,168],"increasing":[2],"adoption":[3],"of":[4,66],"Vehicle-toEverything":[5],"(V2X)":[6],"communication":[7],"in":[8,15,40,142,161],"next-generation":[9],"intelligent":[10],"transportation":[11],"systems,":[12],"ensuring":[13],"cybersecurity":[14,144],"vehicular":[16],"networks":[17,178],"has":[18],"become":[19],"a":[20],"critical":[21,140],"challenge.":[22],"Machine":[23],"Learning":[24],"(ML)-based":[25],"Intrusion":[26],"Detection":[27],"Systems":[28],"(IDS)":[29],"have":[30],"been":[31],"widely":[32],"proposed":[33],"to":[34,48,58,136,175],"detect":[35],"anomalous":[36],"and":[37,99,118,154,157,192],"malicious":[38],"activities":[39],"V2X":[41,82,143,177,198],"networks.":[42,83],"However,":[43],"these":[44],"systems":[45],"are":[46],"vulnerable":[47],"adversarial":[49,67,125,152,163],"attacks,":[50],"where":[51],"an":[52],"attacker":[53],"subtly":[54],"manipulates":[55],"input":[56],"data":[57],"evade":[59],"detection.":[60],"This":[61],"study":[62],"investigates":[63],"impact":[65],"attacks":[68,101,126],"on":[69],"ML-based":[70],"IDS":[71,129,174],"using":[72],"CICIoV2024":[74],"dataset,":[75],"which":[76],"includes":[77],"diverse":[78],"cyber":[79,181],"threats":[80],"targeting":[81],"We":[84],"evaluate":[85],"Fast":[86],"Gradient":[87,92],"Sign":[88],"Method":[89],"(FGSM),":[90],"Projected":[91],"Descent":[93],"(PGD),":[94],"Carlini":[95],"&":[96],"Wagner":[97],"(CW),":[98],"DeepFool":[100],"against":[102],"ML":[103],"models":[104],"such":[105],"as":[106],"Random":[107],"Forest":[108],"(RF),":[109],"Support":[110],"Vector":[111],"Machines":[112],"(SVM),":[113],"Convolutional":[114],"Neural":[115],"Networks":[116],"(CNN),":[117],"Transformer-based":[119],"IDS.":[120],"Our":[121],"results":[122],"demonstrate":[123],"that":[124],"significantly":[127],"degrade":[128],"performance,":[130],"reducing":[131],"detection":[132],"accuracy":[133],"by":[134],"up":[135],"40%,":[137],"thereby":[138],"exposing":[139],"vulnerabilities":[141],"frameworks.":[145],"Furthermore,":[146],"we":[147],"assess":[148],"defence":[149],"mechanisms,":[150],"including":[151],"training":[153],"feature":[155],"squeezing,":[156],"analyze":[158],"their":[159],"effectiveness":[160],"mitigating":[162],"threats.":[164,182],"The":[165],"findings":[166],"highlight":[167],"urgent":[169],"need":[170],"for":[171,197],"robust,":[172],"adversarially-aware":[173],"safeguard":[176],"from":[179],"evolving":[180],"Future":[183],"research":[184],"directions":[185],"include":[186],"realworld":[187],"deployment,":[188],"federated":[189],"learning-based":[190],"IDS,":[191],"adversarially":[193],"robust":[194],"AI":[195],"architectures":[196],"security.":[199]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}