{"id":"https://openalex.org/W7127622509","doi":"https://doi.org/10.1109/ccnc65079.2026.11366546","title":"Distinguishing Sensor Faults and Malicious Attacks in Connected Vehicles Using Machine Learning","display_name":"Distinguishing Sensor Faults and Malicious Attacks in Connected Vehicles Using Machine Learning","publication_year":2026,"publication_date":"2026-01-09","ids":{"openalex":"https://openalex.org/W7127622509","doi":"https://doi.org/10.1109/ccnc65079.2026.11366546"},"language":null,"primary_location":{"id":"doi:10.1109/ccnc65079.2026.11366546","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ccnc65079.2026.11366546","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 IEEE 23rd Consumer Communications &amp;amp; Networking Conference (CCNC)","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/A5121546090","display_name":"Rakesh Das","orcid":null},"institutions":[{"id":"https://openalex.org/I13511017","display_name":"Texas State University","ror":"https://ror.org/05h9q1g27","country_code":"US","type":"education","lineage":["https://openalex.org/I13511017"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Rakesh Das","raw_affiliation_strings":["Texas State University,Department of Computer Science"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Texas State University,Department of Computer Science","institution_ids":["https://openalex.org/I13511017"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057875705","display_name":"Henry Griffith","orcid":"https://orcid.org/0000-0002-4592-8609"},"institutions":[{"id":"https://openalex.org/I38182162","display_name":"Northwest Vista College","ror":"https://ror.org/04jg63q12","country_code":"US","type":"education","lineage":["https://openalex.org/I38182162","https://openalex.org/I4210160641"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Henry Griffith","raw_affiliation_strings":["Northwest Vista College,Department of Engineering"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Northwest Vista College,Department of Engineering","institution_ids":["https://openalex.org/I38182162"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5005136133","display_name":"Heena Rathore","orcid":"https://orcid.org/0000-0002-9403-8071"},"institutions":[{"id":"https://openalex.org/I13511017","display_name":"Texas State University","ror":"https://ror.org/05h9q1g27","country_code":"US","type":"education","lineage":["https://openalex.org/I13511017"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Heena Rathore","raw_affiliation_strings":["Texas State University,Department of Computer Science"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Texas State University,Department of Computer Science","institution_ids":["https://openalex.org/I13511017"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"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":0.08721838,"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":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10761","display_name":"Vehicular Ad Hoc Networks (VANETs)","score":0.18170000612735748,"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":0.18170000612735748,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.10660000145435333,"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.07819999754428864,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"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/random-forest","display_name":"Random forest","score":0.483599990606308},{"id":"https://openalex.org/keywords/wireless-sensor-network","display_name":"Wireless sensor network","score":0.44020000100135803},{"id":"https://openalex.org/keywords/work","display_name":"Work (physics)","score":0.359499990940094},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.3400000035762787},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.32910001277923584},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.29260000586509705}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5715000033378601},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.54339998960495},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.483599990606308},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4754999876022339},{"id":"https://openalex.org/C24590314","wikidata":"https://www.wikidata.org/wiki/Q336038","display_name":"Wireless sensor network","level":2,"score":0.44020000100135803},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.3659999966621399},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.359499990940094},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.3400000035762787},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.32910001277923584},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.30079999566078186},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.29260000586509705},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2818000018596649},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.28110000491142273},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2646999955177307},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.25290000438690186},{"id":"https://openalex.org/C152745839","wikidata":"https://www.wikidata.org/wiki/Q5438153","display_name":"Fault detection and isolation","level":3,"score":0.25220000743865967}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ccnc65079.2026.11366546","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ccnc65079.2026.11366546","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 IEEE 23rd Consumer Communications &amp;amp; Networking Conference (CCNC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.5034818053245544}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W2987245785","https://openalex.org/W3013025041","https://openalex.org/W3044368129","https://openalex.org/W3048207230","https://openalex.org/W3143504837","https://openalex.org/W4200366202","https://openalex.org/W4283167377","https://openalex.org/W4321193231","https://openalex.org/W4366386430","https://openalex.org/W4383738214","https://openalex.org/W4389230716","https://openalex.org/W4392248398","https://openalex.org/W4396909979","https://openalex.org/W4403759306","https://openalex.org/W4404795641","https://openalex.org/W4411010737","https://openalex.org/W4411343543","https://openalex.org/W4413918119"],"related_works":[],"abstract_inverted_index":{"Connected":[0],"and":[1,8,32,39,53,69,73,94,105],"Autonomous":[2],"Vehicles":[3],"(CAVs)":[4],"enhance":[5],"the":[6,49,76,86],"safety":[7,50],"efficiency":[9],"of":[10,51,88],"transportation":[11],"by":[12,60],"communicating":[13],"sensor":[14,30,37,108],"data":[15],"through":[16],"Basic":[17],"Safety":[18],"Messages":[19],"(BSMs).":[20],"This":[21,55],"coordinated":[22],"navigation":[23],"strategy":[24],"is":[25],"vulnerable":[26],"to":[27,44,101],"both":[28,64],"unavoidable":[29],"faults":[31,38,66,109],"intentional":[33],"malicious":[34,40,70,111],"attacks.":[35,112],"Confusing":[36],"attacks":[41,71],"can":[42],"lead":[43],"inappropriate":[45],"responses":[46],"that":[47],"compromise":[48],"passengers":[52],"infrastructure.":[54],"work":[56],"addresses":[57],"this":[58],"issue":[59],"first":[61],"systematically":[62],"introducing":[63],"simulated":[65],"(drift,":[67],"hard-over)":[68],"(colluding":[72],"replay)":[74],"into":[75],"publicly-available":[77],"Tampa":[78],"CV":[79],"Pilot":[80],"BSM":[81],"dataset.":[82],"We":[83],"then":[84],"evaluate":[85],"ability":[87],"various":[89],"classical":[90],"machine":[91],"learning":[92],"approaches":[93],"ensemble":[95],"classifiers":[96],"like":[97],"Random":[98],"Forest":[99],"(RF)":[100],"automatically":[102],"detect":[103],"anomalies":[104],"distinguish":[106],"between":[107],"from":[110]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2026-02-06T00:00:00"}
