{"id":"https://openalex.org/W4290996520","doi":"https://doi.org/10.1109/icc45855.2022.9838734","title":"Deep Learning Driven Security in Digital Twins of Drone Network","display_name":"Deep Learning Driven Security in Digital Twins of Drone Network","publication_year":2022,"publication_date":"2022-05-16","ids":{"openalex":"https://openalex.org/W4290996520","doi":"https://doi.org/10.1109/icc45855.2022.9838734"},"language":"en","primary_location":{"id":"doi:10.1109/icc45855.2022.9838734","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icc45855.2022.9838734","pdf_url":null,"source":{"id":"https://openalex.org/S4363607711","display_name":"ICC 2022 - IEEE International Conference on Communications","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICC 2022 - IEEE International Conference on Communications","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/A5074317404","display_name":"Jingyi Wu","orcid":"https://orcid.org/0000-0001-8283-0646"},"institutions":[{"id":"https://openalex.org/I108688024","display_name":"Qingdao University","ror":"https://ror.org/021cj6z65","country_code":"CN","type":"education","lineage":["https://openalex.org/I108688024"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jingyi Wu","raw_affiliation_strings":["Qingdao University,School of Computer Science,Qingdao,China","School of Computer Science, Qingdao University, Qingdao, China"],"affiliations":[{"raw_affiliation_string":"Qingdao University,School of Computer Science,Qingdao,China","institution_ids":["https://openalex.org/I108688024"]},{"raw_affiliation_string":"School of Computer Science, Qingdao University, Qingdao, China","institution_ids":["https://openalex.org/I108688024"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054640178","display_name":"Jinkang Guo","orcid":"https://orcid.org/0000-0003-2633-6314"},"institutions":[{"id":"https://openalex.org/I108688024","display_name":"Qingdao University","ror":"https://ror.org/021cj6z65","country_code":"CN","type":"education","lineage":["https://openalex.org/I108688024"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinkang Guo","raw_affiliation_strings":["Qingdao University,School of Computer Science,Qingdao,China","School of Computer Science, Qingdao University, Qingdao, China"],"affiliations":[{"raw_affiliation_string":"Qingdao University,School of Computer Science,Qingdao,China","institution_ids":["https://openalex.org/I108688024"]},{"raw_affiliation_string":"School of Computer Science, Qingdao University, Qingdao, China","institution_ids":["https://openalex.org/I108688024"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5108044017","display_name":"Zhihan Lv","orcid":"https://orcid.org/0009-0005-9452-5768"},"institutions":[{"id":"https://openalex.org/I108688024","display_name":"Qingdao University","ror":"https://ror.org/021cj6z65","country_code":"CN","type":"education","lineage":["https://openalex.org/I108688024"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhihan Lv","raw_affiliation_strings":["Qingdao University,School of Computer Science,Qingdao,China","School of Computer Science, Qingdao University, Qingdao, China"],"affiliations":[{"raw_affiliation_string":"Qingdao University,School of Computer Science,Qingdao,China","institution_ids":["https://openalex.org/I108688024"]},{"raw_affiliation_string":"School of Computer Science, Qingdao University, Qingdao, China","institution_ids":["https://openalex.org/I108688024"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5074317404"],"corresponding_institution_ids":["https://openalex.org/I108688024"],"apc_list":null,"apc_paid":null,"fwci":1.1427,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":{"value":0.80031243,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9562000036239624,"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":0.9562000036239624,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.95169997215271,"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/T13918","display_name":"Advanced Data and IoT Technologies","score":0.9334999918937683,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/drone","display_name":"Drone","score":0.8754850625991821},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7896526455879211},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6083570718765259},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5158414244651794},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.4860892593860626},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4640205502510071},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4180991053581238},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4158041179180145}],"concepts":[{"id":"https://openalex.org/C59519942","wikidata":"https://www.wikidata.org/wiki/Q650665","display_name":"Drone","level":2,"score":0.8754850625991821},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7896526455879211},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6083570718765259},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5158414244651794},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.4860892593860626},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4640205502510071},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4180991053581238},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4158041179180145},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C54355233","wikidata":"https://www.wikidata.org/wiki/Q7162","display_name":"Genetics","level":1,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icc45855.2022.9838734","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icc45855.2022.9838734","pdf_url":null,"source":{"id":"https://openalex.org/S4363607711","display_name":"ICC 2022 - IEEE International Conference on Communications","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICC 2022 - IEEE International Conference on Communications","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4699999988079071,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W2774492100","https://openalex.org/W2896880663","https://openalex.org/W2907834603","https://openalex.org/W2965894868","https://openalex.org/W2967354244","https://openalex.org/W3017305623","https://openalex.org/W3017752721","https://openalex.org/W3041983853","https://openalex.org/W3085946920","https://openalex.org/W3094215927"],"related_works":["https://openalex.org/W4312200629","https://openalex.org/W4220882927","https://openalex.org/W4223943233","https://openalex.org/W4360585206","https://openalex.org/W4364306694","https://openalex.org/W4311954163","https://openalex.org/W4309045103","https://openalex.org/W4225161397","https://openalex.org/W3014300295","https://openalex.org/W2995227436"],"abstract_inverted_index":{"This":[0],"study":[1,33],"aims":[2],"to":[3,42,80,92,133,140],"explore":[4],"the":[5,21,25,35,44,53,57,62,65,69,72,86,94,98,101,122,135,146,152,167,174,178,182,192,198,208,224,235],"security":[6,22,237],"issues":[7,23],"and":[8,85,104,156,219,238],"computational":[9],"intelligence":[10],"of":[11,24,55,61,97,173,184,226,241],"drone":[12,26,99,110,210,242],"information":[13],"system":[14,27,47,63],"based":[15,116],"on":[16,117],"deep":[17],"learning.":[18],"Targeting":[19],"at":[20],"when":[28,181],"it":[29],"is":[30,78,90,114,127,149,177,188,201],"attacked,":[31],"this":[32],"adopts":[34],"improved":[36],"long":[37],"short-term":[38],"memory":[39],"(LSTM)":[40],"network":[41],"analyze":[43],"cyber":[45],"physical":[46,102],"(CPS)":[48],"data":[49,60,82],"for":[50,109,234],"prediction":[51,107,212,217],"from":[52],"perspective":[54],"predicting":[56],"control":[58],"signal":[59],"before":[64],"attack":[66,106,211],"occurs.":[67],"At":[68],"same":[70],"time,":[71],"differential":[73,118],"privacy":[74,83],"frequent":[75],"subgraph":[76],"(DPFS)":[77],"introduced":[79],"keep":[81],"confidential,":[84],"digital":[87,111],"twins":[88,112],"technology":[89],"used":[91],"map":[93],"operating":[95],"environment":[96],"in":[100],"space,":[103],"an":[105],"model":[108,137,148,176,213],"CPS":[113],"constructed":[115,136,209],"privacy-improved":[119],"LSTM.":[120],"Finally,":[121],"tennessee":[123],"eastman":[124],"(TE)":[125],"process":[126],"undertaken":[128],"as":[129,139],"a":[130],"simulation":[131],"platform":[132],"simulate":[134],"so":[138],"verify":[141],"its":[142],"performance.":[143],"In":[144],"addition,":[145],"proposed":[147,159,175,199],"compared":[150],"with":[151,191,204],"Bidirectional":[153],"LSTM":[154],"(BiLSTM)":[155],"Attention-BiLSTM":[157],"models":[158],"by":[160],"other":[161],"scholars.":[162],"It":[163],"was":[164],"found":[165],"that":[166,197],"root":[168],"mean":[169],"square":[170],"error":[171],"(RMSE)":[172],"smallest":[179],"(0.20)":[180],"number":[183],"hidden":[185],"layer":[186],"nodes":[187],"26.":[189],"Comparison":[190],"actual":[193],"flow":[194],"value":[195],"shows":[196],"algorithm":[200],"more":[202],"accurate":[203],"better":[205,221],"fitting.":[206],"Therefore,":[207],"can":[214,230],"achieve":[215],"higher":[216],"accuracy":[218],"obvious":[220],"robustness":[222],"under":[223],"premise":[225],"ensuring":[227],"errors,":[228],"which":[229],"provide":[231],"experimental":[232],"basis":[233],"later":[236],"intelligent":[239],"development":[240],"system.":[243]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
