{"id":"https://openalex.org/W3092255243","doi":"https://doi.org/10.1109/dac18072.2020.9218557","title":"Exploring Inherent Sensor Redundancy for Automotive Anomaly Detection","display_name":"Exploring Inherent Sensor Redundancy for Automotive Anomaly Detection","publication_year":2020,"publication_date":"2020-07-01","ids":{"openalex":"https://openalex.org/W3092255243","doi":"https://doi.org/10.1109/dac18072.2020.9218557","mag":"3092255243"},"language":"en","primary_location":{"id":"doi:10.1109/dac18072.2020.9218557","doi":"https://doi.org/10.1109/dac18072.2020.9218557","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dac18072.2020.9218557","pdf_url":null,"source":{"id":"https://openalex.org/S4210231368","display_name":"ACM/IEEE Design Automation Conference (DAC)","issn_l":"0738-100X","issn":["0738-100X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core"],"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 57th ACM/IEEE Design Automation Conference (DAC)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"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/A5086222589","display_name":"Tianjia He","orcid":"https://orcid.org/0000-0002-9840-5676","observed_orcids":["https://orcid.org/0000-0002-9840-5676"]},"institutions":[{"id":"https://openalex.org/I70983195","display_name":"Syracuse University","ror":"https://ror.org/025r5qe02","country_code":"US","type":"education","lineage":["https://openalex.org/I70983195"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tianjia He","raw_affiliation_strings":["Department of Electrical Engineering and Computer Science, Syracuse University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering and Computer Science, Syracuse University","institution_ids":["https://openalex.org/I70983195"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100351950","display_name":"Lin Zhang","orcid":"https://orcid.org/0000-0003-3708-9056","observed_orcids":["https://orcid.org/0000-0003-3708-9056"]},"institutions":[{"id":"https://openalex.org/I70983195","display_name":"Syracuse University","ror":"https://ror.org/025r5qe02","country_code":"US","type":"education","lineage":["https://openalex.org/I70983195"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lin Zhang","raw_affiliation_strings":["Department of Electrical Engineering and Computer Science, Syracuse University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering and Computer Science, Syracuse University","institution_ids":["https://openalex.org/I70983195"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007560139","display_name":"Fanxin Kong","orcid":"https://orcid.org/0000-0002-2174-1620","observed_orcids":["https://orcid.org/0000-0002-2174-1620"]},"institutions":[{"id":"https://openalex.org/I70983195","display_name":"Syracuse University","ror":"https://ror.org/025r5qe02","country_code":"US","type":"education","lineage":["https://openalex.org/I70983195"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Fanxin Kong","raw_affiliation_strings":["Department of Electrical Engineering and Computer Science, Syracuse University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering and Computer Science, Syracuse University","institution_ids":["https://openalex.org/I70983195"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5065842715","display_name":"Asif Salekin","orcid":"https://orcid.org/0000-0002-0807-8967","observed_orcids":["https://orcid.org/0000-0002-0807-8967"]},"institutions":[{"id":"https://openalex.org/I70983195","display_name":"Syracuse University","ror":"https://ror.org/025r5qe02","country_code":"US","type":"education","lineage":["https://openalex.org/I70983195"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Asif Salekin","raw_affiliation_strings":["Department of Electrical Engineering and Computer Science, Syracuse University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering and Computer Science, Syracuse University","institution_ids":["https://openalex.org/I70983195"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I70983195"],"apc_list":null,"apc_paid":null,"fwci":2.2394,"has_fulltext":false,"cited_by_count":51,"citation_normalized_percentile":{"value":0.90801674,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":88,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.5195000171661377,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.5195000171661377,"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/T10400","display_name":"Network Security and Intrusion Detection","score":0.15690000355243683,"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/T10876","display_name":"Fault Detection and Control Systems","score":0.10279999673366547,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/sensor-redundancy","display_name":"sensor redundancy","score":0.9449999928474426},{"id":"https://openalex.org/keywords/heterogeneous-sensors","display_name":"heterogeneous sensors","score":0.6420000195503235},{"id":"https://openalex.org/keywords/deep-autoencoder","display_name":"deep autoencoder","score":0.8809999823570251},{"id":"https://openalex.org/keywords/sensor-spoofing","display_name":"sensor spoofing","score":0.7850000262260437},{"id":"https://openalex.org/keywords/vehicle-sensor-data","display_name":"vehicle sensor data","score":0.6370000243186951},{"id":"https://openalex.org/keywords/autonomous-vehicles","display_name":"autonomous vehicles","score":0.4740000069141388},{"id":"https://openalex.org/keywords/cyber-physical-attacks","display_name":"cyber-physical attacks","score":0.6349999904632568}],"concepts":[{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.7746981382369995},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7484329342842102},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.7256803512573242},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.6386396288871765},{"id":"https://openalex.org/C526921623","wikidata":"https://www.wikidata.org/wiki/Q190117","display_name":"Automotive industry","level":2,"score":0.5063532590866089},{"id":"https://openalex.org/C7545210","wikidata":"https://www.wikidata.org/wiki/Q838123","display_name":"Data redundancy","level":2,"score":0.5038115382194519},{"id":"https://openalex.org/C24590314","wikidata":"https://www.wikidata.org/wiki/Q336038","display_name":"Wireless sensor network","level":2,"score":0.44688528776168823},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.4266544580459595},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.42246437072753906},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.41469651460647583},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3932284712791443},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3589133024215698},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.27091777324676514},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.173456072807312},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.14965331554412842},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"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/dac18072.2020.9218557","doi":"https://doi.org/10.1109/dac18072.2020.9218557","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dac18072.2020.9218557","pdf_url":null,"source":{"id":"https://openalex.org/S4210231368","display_name":"ACM/IEEE Design Automation Conference (DAC)","issn_l":"0738-100X","issn":["0738-100X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core"],"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 57th ACM/IEEE Design Automation Conference (DAC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W185142374","https://openalex.org/W1655377165","https://openalex.org/W1995810576","https://openalex.org/W2056451850","https://openalex.org/W2116520617","https://openalex.org/W2132688659","https://openalex.org/W2145071552","https://openalex.org/W2148974412","https://openalex.org/W2207856858","https://openalex.org/W2545810962","https://openalex.org/W2593182419","https://openalex.org/W2602431082","https://openalex.org/W2756489700","https://openalex.org/W2798493418","https://openalex.org/W2949721659","https://openalex.org/W4393888174"],"related_works":["https://openalex.org/W2602431082","https://openalex.org/W3180026872","https://openalex.org/W4391640464","https://openalex.org/W3162448362","https://openalex.org/W4413245576","https://openalex.org/W2915095159","https://openalex.org/W4394829818","https://openalex.org/W4361275963","https://openalex.org/W4404572882","https://openalex.org/W4413068065"],"abstract_inverted_index":{"The":[0,66,117],"increasing":[1],"autonomy":[2],"and":[3,11,29,107,130,158],"connectivity":[4],"have":[5],"been":[6],"transitioning":[7],"automobiles":[8],"to":[9,17,33,42,74],"complex":[10],"open":[12],"architectures":[13],"that":[14,69,95],"are":[15],"vulnerable":[16],"malicious":[18],"attacks":[19],"beyond":[20],"conventional":[21],"cyber":[22],"attacks.":[23],"Attackers":[24],"may":[25],"non-invasively":[26],"compromise":[27],"sensors":[28,60,71],"spoof":[30],"the":[31,40,75,84,111,115,123,131,156,161],"controller":[32],"perform":[34],"unsafe":[35],"actions.":[36],"This":[37],"concern":[38],"emphasizes":[39],"need":[41],"validate":[43],"sensor":[44,64,102],"data":[45,103,127,146],"before":[46],"acting":[47],"on":[48],"them.":[49],"Unlike":[50],"existing":[51,141],"works,":[52],"this":[53],"paper":[54],"exploits":[55],"inherent":[56],"redundancy":[57,67,85],"among":[58,137],"heterogeneous":[59],"for":[61,114,128],"detecting":[62],"anomalous":[63,126],"measurements.":[65],"is":[68,120],"multiple":[70],"simultaneously":[72],"respond":[73],"same":[76],"physical":[77],"phenomenon":[78],"in":[79,104,140],"a":[80,87,97,144],"related":[81],"fashion.":[82],"Embedding":[83],"into":[86],"deep":[88],"autoencoder,":[89],"we":[90,154],"propose":[91],"an":[92],"anomaly":[93],"detector":[94],"learns":[96],"consistent":[98],"pattern":[99],"from":[100,149],"vehicle":[101,152],"normal":[105],"states":[106],"utilizes":[108],"it":[109],"as":[110,139],"nominal":[112],"behavior":[113],"detection.":[116],"proposed":[118,162],"method":[119],"independent":[121],"of":[122,125,134,151,160],"scarcity":[124],"training":[129],"intensive":[132],"calculation":[133],"pairwise":[135],"correlation":[136],"senors":[138],"works.":[142],"Using":[143],"real-world":[145],"set":[147],"collected":[148],"tens":[150],"sensors,":[153],"demonstrate":[155],"feasibility":[157],"efficacy":[159],"method.":[163]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":14},{"year":2023,"cited_by_count":13},{"year":2022,"cited_by_count":8},{"year":2021,"cited_by_count":6},{"year":2020,"cited_by_count":1}],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2020-10-15T00:00:00"}
