{"id":"https://openalex.org/W2461378669","doi":"https://doi.org/10.1109/vtcspring.2016.7504089","title":"A Novel Intrusion Detection Method Using Deep Neural Network for In-Vehicle Network Security","display_name":"A Novel Intrusion Detection Method Using Deep Neural Network for In-Vehicle Network Security","publication_year":2016,"publication_date":"2016-05-01","ids":{"openalex":"https://openalex.org/W2461378669","doi":"https://doi.org/10.1109/vtcspring.2016.7504089","mag":"2461378669"},"language":"en","primary_location":{"id":"doi:10.1109/vtcspring.2016.7504089","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vtcspring.2016.7504089","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE 83rd Vehicular Technology Conference (VTC 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/A5102004330","display_name":"Minju Kang","orcid":null},"institutions":[{"id":"https://openalex.org/I138925566","display_name":"Ewha Womans University","ror":"https://ror.org/053fp5c05","country_code":"KR","type":"education","lineage":["https://openalex.org/I138925566"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Min-Ju Kang","raw_affiliation_strings":["Department of Electronics Engineering, Ewha W. University, Seoul, Republic of Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronics Engineering, Ewha W. University, Seoul, Republic of Korea","institution_ids":["https://openalex.org/I138925566"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5039192390","display_name":"Je\u2010Won Kang","orcid":"https://orcid.org/0000-0002-1637-9479"},"institutions":[{"id":"https://openalex.org/I138925566","display_name":"Ewha Womans University","ror":"https://ror.org/053fp5c05","country_code":"KR","type":"education","lineage":["https://openalex.org/I138925566"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Je-Won Kang","raw_affiliation_strings":["Department of Electronics Engineering, Ewha W. University, Seoul, Republic of Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronics Engineering, Ewha W. University, Seoul, Republic of Korea","institution_ids":["https://openalex.org/I138925566"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I138925566"],"apc_list":null,"apc_paid":null,"fwci":12.6251,"has_fulltext":false,"cited_by_count":167,"citation_normalized_percentile":{"value":0.99428808,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10400","display_name":"Network Security and Intrusion Detection","score":0.9995999932289124,"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":0.9995999932289124,"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/T10761","display_name":"Vehicular Ad Hoc Networks (VANETs)","score":0.9983999729156494,"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":0.9975000023841858,"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/computer-science","display_name":"Computer science","score":0.8367106914520264},{"id":"https://openalex.org/keywords/network-packet","display_name":"Network packet","score":0.771431565284729},{"id":"https://openalex.org/keywords/intrusion-detection-system","display_name":"Intrusion detection system","score":0.6558321118354797},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.561526894569397},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.47396227717399597},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.46654224395751953},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.46039432287216187},{"id":"https://openalex.org/keywords/bitstream","display_name":"Bitstream","score":0.455089807510376},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.42554157972335815},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.39394432306289673},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.09051814675331116}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8367106914520264},{"id":"https://openalex.org/C158379750","wikidata":"https://www.wikidata.org/wiki/Q214111","display_name":"Network packet","level":2,"score":0.771431565284729},{"id":"https://openalex.org/C35525427","wikidata":"https://www.wikidata.org/wiki/Q745881","display_name":"Intrusion detection system","level":2,"score":0.6558321118354797},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.561526894569397},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.47396227717399597},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.46654224395751953},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.46039432287216187},{"id":"https://openalex.org/C136695289","wikidata":"https://www.wikidata.org/wiki/Q415568","display_name":"Bitstream","level":3,"score":0.455089807510376},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.42554157972335815},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.39394432306289673},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.09051814675331116},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/vtcspring.2016.7504089","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vtcspring.2016.7504089","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE 83rd Vehicular Technology Conference (VTC Spring)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7099999785423279,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W1993470778","https://openalex.org/W2065806894","https://openalex.org/W2106958114","https://openalex.org/W2114085411","https://openalex.org/W2116520617","https://openalex.org/W2133854595","https://openalex.org/W2138857742","https://openalex.org/W2142347946","https://openalex.org/W2160815625","https://openalex.org/W2163605009","https://openalex.org/W6681340174","https://openalex.org/W6684191040","https://openalex.org/W6950685506"],"related_works":["https://openalex.org/W2119626116","https://openalex.org/W1994765432","https://openalex.org/W4393107225","https://openalex.org/W2005006301","https://openalex.org/W2352380146","https://openalex.org/W2037887240","https://openalex.org/W2130100961","https://openalex.org/W2186699851","https://openalex.org/W1998137069","https://openalex.org/W1987970309"],"abstract_inverted_index":{"In":[0,16],"this":[1],"paper,":[2],"we":[3],"propose":[4],"a":[5,11,59,83,90],"novel":[6],"intrusion":[7],"detection":[8,93],"technique":[9,66],"using":[10],"deep":[12],"neural":[13],"network":[14,21,74],"(DNN).":[15],"the":[17,62,72,76,87],"proposed":[18,65],"technique,":[19],"in-vehicle":[20],"packets":[22],"exchanged":[23],"between":[24],"electronic":[25],"control":[26],"units":[27],"(ECU)":[28],"are":[29,55,78],"trained":[30,79],"to":[31,86],"extract":[32],"low-":[33],"dimensional":[34],"features":[35,45],"and":[36,41,50,81],"used":[37],"for":[38],"discriminating":[39],"normal":[40],"hacking":[42],"packets.":[43],"The":[44,64],"perform":[46],"in":[47,71,95],"high":[48,92],"efficient":[49],"low":[51],"complexity":[52],"because":[53],"they":[54],"generated":[56],"directly":[57],"from":[58],"bitstream":[60],"over":[61],"network.":[63],"monitors":[67],"an":[68],"exchanging":[69],"packet":[70],"vehicular":[73],"while":[75],"feature":[77],"off-line,":[80],"provides":[82],"real-time":[84],"response":[85],"attack":[88],"with":[89],"significantly":[91],"ratio":[94],"our":[96],"experiments.":[97]},"counts_by_year":[{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":25},{"year":2022,"cited_by_count":34},{"year":2021,"cited_by_count":24},{"year":2020,"cited_by_count":23},{"year":2019,"cited_by_count":29},{"year":2018,"cited_by_count":12},{"year":2017,"cited_by_count":9}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
