{"id":"https://openalex.org/W4408565426","doi":"https://doi.org/10.1109/icdmw65004.2024.00052","title":"Trust-based positional forgery detection in AI-driven autonomous intersection management using hybrid graph-based reinforcement learning","display_name":"Trust-based positional forgery detection in AI-driven autonomous intersection management using hybrid graph-based reinforcement learning","publication_year":2024,"publication_date":"2024-12-09","ids":{"openalex":"https://openalex.org/W4408565426","doi":"https://doi.org/10.1109/icdmw65004.2024.00052"},"language":"en","primary_location":{"id":"doi:10.1109/icdmw65004.2024.00052","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icdmw65004.2024.00052","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Data Mining Workshops (ICDMW)","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/A5065875301","display_name":"Shajina Anand","orcid":"https://orcid.org/0000-0001-6721-1150"},"institutions":[{"id":"https://openalex.org/I12524447","display_name":"Seton Hall University","ror":"https://ror.org/007tn5k56","country_code":"US","type":"education","lineage":["https://openalex.org/I12524447"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Shajina Anand","raw_affiliation_strings":["Seton Hall University,Department of Mathematics and Computer Science"],"affiliations":[{"raw_affiliation_string":"Seton Hall University,Department of Mathematics and Computer Science","institution_ids":["https://openalex.org/I12524447"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5065875301"],"corresponding_institution_ids":["https://openalex.org/I12524447"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.25605775,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"357","last_page":"363"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9925000071525574,"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"}},"topics":[{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9925000071525574,"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/reinforcement-learning","display_name":"Reinforcement learning","score":0.8138483166694641},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7604089975357056},{"id":"https://openalex.org/keywords/intersection","display_name":"Intersection (aeronautics)","score":0.6121647357940674},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5341991782188416},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5066445469856262},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.24536055326461792},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.09670892357826233}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.8138483166694641},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7604089975357056},{"id":"https://openalex.org/C64543145","wikidata":"https://www.wikidata.org/wiki/Q162942","display_name":"Intersection (aeronautics)","level":2,"score":0.6121647357940674},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5341991782188416},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5066445469856262},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.24536055326461792},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.09670892357826233},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icdmw65004.2024.00052","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icdmw65004.2024.00052","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Data Mining Workshops (ICDMW)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.4399999976158142,"id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W2910166370","https://openalex.org/W2946793016","https://openalex.org/W3112379500","https://openalex.org/W3122846486","https://openalex.org/W4285254796","https://openalex.org/W4366492996","https://openalex.org/W4392939528","https://openalex.org/W4400616877","https://openalex.org/W4401056522","https://openalex.org/W4401328639","https://openalex.org/W6870085043"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4306904969","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2138720691","https://openalex.org/W2376932109"],"abstract_inverted_index":{"Positional":[0,35],"forgery":[1],"by":[2,113],"malicious":[3],"vehicles":[4],"can":[5],"severely":[6],"disrupt":[7],"AI-driven":[8],"autonomous":[9,140],"intersection":[10,116],"management":[11,134],"systems,":[12,128],"leading":[13],"to":[14,49,93,98,102,130],"inaccurate":[15],"traffic":[16,110,127,133],"predictions":[17],"and":[18,73,95,123,135],"increased":[19,136],"accident":[20],"risks.":[21],"Traditional":[22],"detection":[23,84],"methods":[24],"struggle":[25],"in":[26,139],"multi-tiered":[27],"vehicular":[28],"networks.":[29],"This":[30,118],"paper":[31],"presents":[32],"a":[33,42,83],"Trust-Based":[34],"Forgery":[36],"Detection":[37],"(TPFD)":[38],"system":[39,54,106],"that":[40],"leverages":[41],"Hybrid":[43],"Graph-based":[44],"Reinforcement":[45,74],"Learning":[46,75],"(HGRL)":[47],"framework":[48],"combat":[50],"positional":[51],"forgery.":[52],"The":[53,105],"integrates":[55],"Graph":[56],"Neural":[57,66],"Networks":[58,67],"(GNNs)":[59],"for":[60,69,77],"modelling":[61],"Vehicle-to-Everything":[62],"(V2X)":[63],"communication,":[64],"Recurrent":[65],"(RNNs)":[68],"analysing":[70],"temporal":[71],"data,":[72],"(RL)":[76],"dynamic":[78],"trust":[79,138],"evaluation.":[80],"TPFD":[81],"achieved":[82],"accuracy":[85],"of":[86,100,125],"98.3%,":[87],"reduced":[88,108],"the":[89,121],"false":[90],"positive":[91],"rate":[92],"3.5%,":[94],"scaled":[96],"efficiently":[97],"networks":[99],"up":[101],"10,000":[103],"vehicles.":[104,141],"also":[107],"hazardous":[109],"gap":[111],"errors":[112],"30%,":[114],"enhancing":[115],"safety.":[117],"research":[119],"strengthens":[120],"reliability":[122],"security":[124],"AI-based":[126],"contributing":[129],"more":[131],"efficient":[132],"public":[137]},"counts_by_year":[],"updated_date":"2025-12-21T23:12:01.093139","created_date":"2025-10-10T00:00:00"}
