{"id":"https://openalex.org/W7117646569","doi":"https://doi.org/10.1145/3773274.3774695","title":"A Deep Learning Approach to Detecting Multiple Types of Sybil Nodes in VANETs","display_name":"A Deep Learning Approach to Detecting Multiple Types of Sybil Nodes in VANETs","publication_year":2025,"publication_date":"2025-12-01","ids":{"openalex":"https://openalex.org/W7117646569","doi":"https://doi.org/10.1145/3773274.3774695"},"language":null,"primary_location":{"id":"doi:10.1145/3773274.3774695","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3773274.3774695","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 18th IEEE/ACM International Conference on Utility and Cloud Computing","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3773274.3774695","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5121600334","display_name":"Dong Tang","orcid":null},"institutions":[{"id":"https://openalex.org/I154130895","display_name":"University of Auckland","ror":"https://ror.org/03b94tp07","country_code":"NZ","type":"education","lineage":["https://openalex.org/I154130895"]}],"countries":["NZ"],"is_corresponding":false,"raw_author_name":"Dong Tang","raw_affiliation_strings":["The University of Auckland, Auckland, New Zealand"],"raw_orcid":"https://orcid.org/0000-0002-7787-1512","affiliations":[{"raw_affiliation_string":"The University of Auckland, Auckland, New Zealand","institution_ids":["https://openalex.org/I154130895"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046437897","display_name":"Aniket Mahanti","orcid":"https://orcid.org/0000-0002-6545-3073"},"institutions":[{"id":"https://openalex.org/I154130895","display_name":"University of Auckland","ror":"https://ror.org/03b94tp07","country_code":"NZ","type":"education","lineage":["https://openalex.org/I154130895"]}],"countries":["NZ"],"is_corresponding":false,"raw_author_name":"Aniket Mahanti","raw_affiliation_strings":["The University of Auckland, Auckland, New Zealand"],"raw_orcid":"https://orcid.org/0000-0002-6545-3073","affiliations":[{"raw_affiliation_string":"The University of Auckland, Auckland, New Zealand","institution_ids":["https://openalex.org/I154130895"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054337099","display_name":"Ranesh Kumar Naha","orcid":"https://orcid.org/0000-0003-4165-9349"},"institutions":[{"id":"https://openalex.org/I160993911","display_name":"Queensland University of Technology","ror":"https://ror.org/03pnv4752","country_code":"AU","type":"education","lineage":["https://openalex.org/I160993911"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Ranesh Naha","raw_affiliation_strings":["Queensland University of Technology, Brisbane, Queensland, Australia"],"raw_orcid":"https://orcid.org/0000-0003-4165-9349","affiliations":[{"raw_affiliation_string":"Queensland University of Technology, Brisbane, Queensland, Australia","institution_ids":["https://openalex.org/I160993911"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5121619428","display_name":"Vishwambhar Pathak","orcid":null},"institutions":[{"id":"https://openalex.org/I4210113324","display_name":"Birla Institute of Scientific Research","ror":"https://ror.org/0203yhg37","country_code":"IN","type":"facility","lineage":["https://openalex.org/I4210113324"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Vishwambhar Pathak","raw_affiliation_strings":["Birla Institute of Technology, Jaipur, India"],"raw_orcid":"https://orcid.org/0000-0001-7174-1138","affiliations":[{"raw_affiliation_string":"Birla Institute of Technology, Jaipur, India","institution_ids":["https://openalex.org/I4210113324"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5111421962","display_name":"Mei Gong","orcid":"https://orcid.org/0009-0004-4415-2142"},"institutions":[{"id":"https://openalex.org/I870124129","display_name":"Mount Royal University","ror":"https://ror.org/04evsam41","country_code":"CA","type":"education","lineage":["https://openalex.org/I870124129"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Mingwei Gong","raw_affiliation_strings":["Mount Royal University, Calgary, Canada"],"raw_orcid":"https://orcid.org/0009-0004-4415-2142","affiliations":[{"raw_affiliation_string":"Mount Royal University, Calgary, Canada","institution_ids":["https://openalex.org/I870124129"]}]}],"institutions":[],"countries_distinct_count":4,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"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/T10761","display_name":"Vehicular Ad Hoc Networks (VANETs)","score":0.9581000208854675,"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":0.9581000208854675,"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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.003700000001117587,"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/T10400","display_name":"Network Security and Intrusion Detection","score":0.0034000000450760126,"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/sybil-attack","display_name":"Sybil attack","score":0.6432999968528748},{"id":"https://openalex.org/keywords/spoofing-attack","display_name":"Spoofing attack","score":0.6395000219345093},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5530999898910522},{"id":"https://openalex.org/keywords/safer","display_name":"SAFER","score":0.4781999886035919},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4700999855995178},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.45879998803138733},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.4571000039577484},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.39899998903274536},{"id":"https://openalex.org/keywords/credibility","display_name":"Credibility","score":0.3472999930381775}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.801800012588501},{"id":"https://openalex.org/C2777505653","wikidata":"https://www.wikidata.org/wiki/Q4470796","display_name":"Sybil attack","level":3,"score":0.6432999968528748},{"id":"https://openalex.org/C167900197","wikidata":"https://www.wikidata.org/wiki/Q11081100","display_name":"Spoofing attack","level":2,"score":0.6395000219345093},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5530999898910522},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5331000089645386},{"id":"https://openalex.org/C2776654903","wikidata":"https://www.wikidata.org/wiki/Q2601463","display_name":"SAFER","level":2,"score":0.4781999886035919},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4700999855995178},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.45879998803138733},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.4571000039577484},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4007999897003174},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.39899998903274536},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3637999892234802},{"id":"https://openalex.org/C2780224610","wikidata":"https://www.wikidata.org/wiki/Q1530061","display_name":"Credibility","level":2,"score":0.3472999930381775},{"id":"https://openalex.org/C47796450","wikidata":"https://www.wikidata.org/wiki/Q508378","display_name":"Intelligent transportation system","level":2,"score":0.33009999990463257},{"id":"https://openalex.org/C24590314","wikidata":"https://www.wikidata.org/wiki/Q336038","display_name":"Wireless sensor network","level":2,"score":0.32429999113082886},{"id":"https://openalex.org/C140547941","wikidata":"https://www.wikidata.org/wiki/Q7797194","display_name":"Threat model","level":2,"score":0.31869998574256897},{"id":"https://openalex.org/C65856478","wikidata":"https://www.wikidata.org/wiki/Q3991682","display_name":"Attack model","level":2,"score":0.31380000710487366},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.31369999051094055},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.3107999861240387},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.28870001435279846},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.2831999957561493},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2816999852657318},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.27869999408721924},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.2773999869823456},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.2597000002861023},{"id":"https://openalex.org/C182590292","wikidata":"https://www.wikidata.org/wiki/Q989632","display_name":"Network security","level":2,"score":0.2558000087738037},{"id":"https://openalex.org/C2983787585","wikidata":"https://www.wikidata.org/wiki/Q93586","display_name":"Feature matching","level":3,"score":0.25440001487731934},{"id":"https://openalex.org/C2780186347","wikidata":"https://www.wikidata.org/wiki/Q11414","display_name":"Subnetwork","level":2,"score":0.25360000133514404}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3773274.3774695","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3773274.3774695","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 18th IEEE/ACM International Conference on Utility and Cloud Computing","raw_type":"proceedings-article"},{"id":"pmh:oai:researchspace.auckland.ac.nz:2292/72394","is_oa":true,"landing_page_url":"https://hdl.handle.net/2292/72394","pdf_url":null,"source":{"id":"https://openalex.org/S7407055463","display_name":"ResearchSpace (University of Auckland)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I154130895","host_organization_name":"University of Auckland","host_organization_lineage":["https://openalex.org/I154130895"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Thesis"}],"best_oa_location":{"id":"doi:10.1145/3773274.3774695","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3773274.3774695","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 18th IEEE/ACM International Conference on Utility and Cloud Computing","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.4236885607242584,"id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W642003551","https://openalex.org/W2259641022","https://openalex.org/W2346365475","https://openalex.org/W2533344333","https://openalex.org/W2545176003","https://openalex.org/W2581560412","https://openalex.org/W2752671358","https://openalex.org/W2800112063","https://openalex.org/W2886863761","https://openalex.org/W2910871229","https://openalex.org/W2913889054","https://openalex.org/W2943566946","https://openalex.org/W2962853362","https://openalex.org/W2964314709","https://openalex.org/W2967371873","https://openalex.org/W2975214196","https://openalex.org/W2998876315","https://openalex.org/W3045657906","https://openalex.org/W3045918422","https://openalex.org/W3096009936","https://openalex.org/W3128255983","https://openalex.org/W3182890598","https://openalex.org/W3191314598","https://openalex.org/W4205656656","https://openalex.org/W4286300255","https://openalex.org/W4292307379","https://openalex.org/W4296761062","https://openalex.org/W4319166496","https://openalex.org/W4319300220","https://openalex.org/W4353062729","https://openalex.org/W4380886145","https://openalex.org/W4386167351","https://openalex.org/W4394692435","https://openalex.org/W4398241130"],"related_works":[],"abstract_inverted_index":{"Sybil":[0,33,48,162],"attacks":[1],"occur":[2],"when":[3],"a":[4,12,43,140,159],"single":[5],"node":[6],"mimics":[7],"multiple":[8,57],"automobiles":[9],"and":[10,18,31,68,85,101,120,133],"poses":[11],"tremendous":[13],"threat":[14],"to":[15,70,104],"the":[16,77,137],"authenticity":[17],"credibility":[19],"of":[20],"VANETs":[21],"communications.":[22,168],"Existing":[23],"detection":[24,50,163],"methods":[25],"are":[26],"not":[27],"robust":[28],"against":[29],"dynamic":[30,134],"cooperative":[32],"attacks,":[34],"calling":[35],"for":[36,47,117,122,165],"stronger":[37],"defense":[38],"mechanisms.":[39],"This":[40,153],"paper":[41],"proposes":[42],"deep":[44],"learning":[45],"solution":[46,164],"attack":[49,131],"in":[51,150],"VANETs,":[52],"where":[53],"malicious":[54],"nodes":[55],"mimic":[56],"vehicles,":[58],"threatening":[59],"communication":[60],"authenticity.":[61],"Our":[62],"approach":[63],"integrates":[64],"hierarchical":[65],"feature":[66,91,106],"engineering":[67,92],"GNNs":[69],"identify":[71],"spatiotemporal":[72,118],"correlations.":[73],"We":[74],"have":[75],"extended":[76],"VeReMi":[78],"dataset":[79],"by":[80,148],"adding":[81],"preset":[82],"route":[83],"spoofing":[84,88],"periodic":[86],"location":[87],"attacks.":[89],"Hierarchical":[90],"divides":[93],"vehicle":[94],"behavior":[95],"into":[96],"instant":[97],"kinematics,":[98],"short-time":[99],"dynamics,":[100],"long-time":[102],"consistency":[103],"address":[105],"fragmentation.":[107],"A":[108],"GCN_GRU":[109],"hybrid":[110],"model":[111],"is":[112],"introduced,":[113],"combining":[114],"graph":[115],"convolutions":[116],"interactions":[119],"GRUs":[121],"trajectory":[123],"analysis.":[124],"Evaluated":[125],"using":[126],"SUMO-OMNeT++":[127],"simulations":[128],"under":[129],"six":[130],"classes":[132],"Oakland":[135],"traffic,":[136],"framework":[138],"achieves":[139],"99.":[141],"91%":[142],"F1":[143],"score,":[144],"outperforming":[145],"conventional":[146],"models":[147],"10%":[149],"complex":[151],"scenarios.":[152],"work":[154],"advances":[155],"IoV":[156],"security":[157],"with":[158],"fault-tolerant,":[160],"real-time":[161],"safer":[166],"vehicular":[167]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-07-20T07:56:41.581041","created_date":"2025-12-31T00:00:00"}
