{"id":"https://openalex.org/W4388193248","doi":"https://doi.org/10.1109/cns59707.2023.10289004","title":"Adversarial Attacks on Deep Learning-Based UAV Navigation Systems","display_name":"Adversarial Attacks on Deep Learning-Based UAV Navigation Systems","publication_year":2023,"publication_date":"2023-10-02","ids":{"openalex":"https://openalex.org/W4388193248","doi":"https://doi.org/10.1109/cns59707.2023.10289004"},"language":"en","primary_location":{"id":"doi:10.1109/cns59707.2023.10289004","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/cns59707.2023.10289004","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE Conference on Communications and Network Security (CNS)","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/A5088956116","display_name":"Mohammed Mynuddin","orcid":"https://orcid.org/0000-0001-7898-5485"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Mohammed Mynuddin","raw_affiliation_strings":["University of North Carolina A&T,Electrical and Computer Engineering,NC,USA"],"affiliations":[{"raw_affiliation_string":"University of North Carolina A&T,Electrical and Computer Engineering,NC,USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033835553","display_name":"Sultan Uddin Khan","orcid":"https://orcid.org/0009-0002-2171-7177"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sultan Uddin Khan","raw_affiliation_strings":["University of North Carolina A&T,Electrical and Computer Engineering,NC,USA"],"affiliations":[{"raw_affiliation_string":"University of North Carolina A&T,Electrical and Computer Engineering,NC,USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":null,"display_name":"Mahmoud Nabil Mahmoud","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mahmoud Nabil Mahmoud","raw_affiliation_strings":["University of North Carolina A&T,Electrical and Computer Engineering,NC,USA"],"affiliations":[{"raw_affiliation_string":"University of North Carolina A&T,Electrical and Computer Engineering,NC,USA","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5012619578","display_name":"Ahmad Alsharif","orcid":"https://orcid.org/0000-0003-1060-1953"},"institutions":[{"id":"https://openalex.org/I17301866","display_name":"University of Alabama","ror":"https://ror.org/03xrrjk67","country_code":"US","type":"education","lineage":["https://openalex.org/I17301866"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ahmad Alsharif","raw_affiliation_strings":["University of Alabama,Department of Computer Science,AL,USA","Department of Computer Science, University of Alabama, AL, USA"],"affiliations":[{"raw_affiliation_string":"University of Alabama,Department of Computer Science,AL,USA","institution_ids":["https://openalex.org/I17301866"]},{"raw_affiliation_string":"Department of Computer Science, University of Alabama, AL, USA","institution_ids":["https://openalex.org/I17301866"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5088956116"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.1713,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.57877016,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"54","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":0.9998999834060669,"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":0.9998999834060669,"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/T12158","display_name":"Guidance and Control Systems","score":0.9277999997138977,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace 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/T11515","display_name":"Bacillus and Francisella bacterial research","score":0.9110000133514404,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.8120355606079102},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7445106506347656},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5427861213684082},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5108787417411804},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.4177256226539612},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.325599730014801}],"concepts":[{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.8120355606079102},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7445106506347656},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5427861213684082},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5108787417411804},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.4177256226539612},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.325599730014801}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cns59707.2023.10289004","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/cns59707.2023.10289004","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE Conference on Communications and Network Security (CNS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W1673923490","https://openalex.org/W1945616565","https://openalex.org/W2543927648","https://openalex.org/W2788239209","https://openalex.org/W2963448658","https://openalex.org/W2963542245","https://openalex.org/W2963855547","https://openalex.org/W3025279871","https://openalex.org/W3160683614","https://openalex.org/W3196302005","https://openalex.org/W4207085178","https://openalex.org/W4221138784","https://openalex.org/W4307371716","https://openalex.org/W4386214319","https://openalex.org/W6637162671","https://openalex.org/W6640425456","https://openalex.org/W6800387725"],"related_works":["https://openalex.org/W2731899572","https://openalex.org/W3215138031","https://openalex.org/W2058170566","https://openalex.org/W2755342338","https://openalex.org/W2772917594","https://openalex.org/W2775347418","https://openalex.org/W2166024367","https://openalex.org/W3009238340","https://openalex.org/W3116076068","https://openalex.org/W2229312674"],"abstract_inverted_index":{"In":[0,84],"recent":[1],"years,":[2],"the":[3,25,32,44,77,89,138,156,170,175,182,188,195],"prevalence":[4],"of":[5,34,50,153,197],"unmanned":[6],"aerial":[7],"vehicles":[8],"(UAVs)":[9],"has":[10,36,55],"grown":[11],"significantly":[12],"in":[13,69,109,160,164],"both":[14],"military":[15],"and":[16,24,64,96,124,141,173,203],"civilian":[17],"settings":[18],"due":[19],"to":[20,27],"their":[21],"versatile":[22],"capabilities":[23],"ability":[26],"undertake":[28],"various":[29],"tasks.":[30],"Nonetheless,":[31],"rise":[33],"UAVs":[35,95,119],"also":[37],"brought":[38],"about":[39],"a":[40,57,113,150,161],"significant":[41,162],"concern":[42],"-":[43],"cybersecurity":[45,72],"aspect.":[46],"The":[47],"increasing":[48],"number":[49,152],"cyberattacks":[51],"targeting":[52],"drone":[53],"systems":[54],"become":[56],"major":[58],"issue.":[59],"Unfortunately,":[60],"inadequate":[61],"vulnerability":[62],"assessments":[63],"security":[65],"countermeasures":[66],"have":[67],"resulted":[68],"numerous":[70],"serious":[71],"breaches":[73],"on":[74,92,105,128,200],"UAVs,":[75],"highlighting":[76],"urgent":[78],"need":[79],"for":[80,100,137,145,169,181],"enhanced":[81],"protection":[82],"measures.":[83],"this":[85],"paper,":[86],"we":[87,193],"investigate":[88],"adversarial":[90,103,134],"attacks":[91,104],"Deep":[93],"Learning-based":[94],"present":[97],"five":[98],"methods":[99],"carrying":[101],"out":[102],"DroNet":[106,111],"model":[107,165,201],"used":[108],"UAVs.":[110],"is":[112],"convolutional":[114],"neural":[115],"network":[116],"that":[117],"guides":[118],"by":[120,167,191],"predicting":[121],"steering":[122,142,183],"angles":[123],"collision":[125,139,171],"probabilities":[126],"based":[127],"camera":[129],"images.":[130],"We":[131],"created":[132],"several":[133],"input":[135],"images":[136],"data":[140,144],"angle":[143,184],"attacking":[146],"purposes.":[147],"By":[148],"perturbing":[149],"small":[151],"samples":[154],"from":[155],"clean":[157],"dataset,":[158,172,185],"resulting":[159],"reduction":[163],"performance":[166,202],"18.66%":[168],"examining":[174],"root":[176],"mean":[177],"square":[178],"error":[179,189,204],"rate":[180,190],"which":[186],"increased":[187],"427.52%,":[192],"observed":[194],"impact":[196],"sample":[198],"perturbation":[199],"rates.":[205]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2026-04-21T08:09:41.155169","created_date":"2025-10-10T00:00:00"}