{"id":"https://openalex.org/W4390190112","doi":"https://doi.org/10.1109/milcom58377.2023.10356224","title":"Adversarial Attacks on LoRa Device Identification and Rogue Signal Detection with Deep Learning","display_name":"Adversarial Attacks on LoRa Device Identification and Rogue Signal Detection with Deep Learning","publication_year":2023,"publication_date":"2023-10-30","ids":{"openalex":"https://openalex.org/W4390190112","doi":"https://doi.org/10.1109/milcom58377.2023.10356224"},"language":"en","primary_location":{"id":"doi:10.1109/milcom58377.2023.10356224","is_oa":false,"landing_page_url":"https://doi.org/10.1109/milcom58377.2023.10356224","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"MILCOM 2023 - 2023 IEEE Military Communications Conference (MILCOM)","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/A5089570143","display_name":"Yalin E. Sagduyu","orcid":"https://orcid.org/0000-0003-1576-5527"},"institutions":[{"id":"https://openalex.org/I859038795","display_name":"Virginia Tech","ror":"https://ror.org/02smfhw86","country_code":"US","type":"education","lineage":["https://openalex.org/I859038795"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yalin E. Sagduyu","raw_affiliation_strings":["Virginia Tech,Arlington,VA,USA","Virginia Tech, Arlington, VA, USA"],"affiliations":[{"raw_affiliation_string":"Virginia Tech,Arlington,VA,USA","institution_ids":["https://openalex.org/I859038795"]},{"raw_affiliation_string":"Virginia Tech, Arlington, VA, USA","institution_ids":["https://openalex.org/I859038795"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5024995674","display_name":"Tugba Erpek","orcid":"https://orcid.org/0000-0003-0168-2587"},"institutions":[{"id":"https://openalex.org/I859038795","display_name":"Virginia Tech","ror":"https://ror.org/02smfhw86","country_code":"US","type":"education","lineage":["https://openalex.org/I859038795"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tugba Erpek","raw_affiliation_strings":["Virginia Tech,Arlington,VA,USA","Virginia Tech, Arlington, VA, USA"],"affiliations":[{"raw_affiliation_string":"Virginia Tech,Arlington,VA,USA","institution_ids":["https://openalex.org/I859038795"]},{"raw_affiliation_string":"Virginia Tech, Arlington, VA, USA","institution_ids":["https://openalex.org/I859038795"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5089570143"],"corresponding_institution_ids":["https://openalex.org/I859038795"],"apc_list":null,"apc_paid":null,"fwci":0.8741,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.79740403,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"385","last_page":"390"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12131","display_name":"Wireless Signal Modulation Classification","score":0.9997000098228455,"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/T12131","display_name":"Wireless Signal Modulation Classification","score":0.9997000098228455,"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/T12357","display_name":"Digital Media Forensic Detection","score":0.9886000156402588,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10964","display_name":"Wireless Communication Security Techniques","score":0.9843999743461609,"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/computer-science","display_name":"Computer science","score":0.8082783222198486},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.7222544550895691},{"id":"https://openalex.org/keywords/spoofing-attack","display_name":"Spoofing attack","score":0.653061032295227},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6402732729911804},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6143151521682739},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.565245509147644},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5286641120910645},{"id":"https://openalex.org/keywords/adversarial-machine-learning","display_name":"Adversarial machine learning","score":0.5246827006340027},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5206750631332397},{"id":"https://openalex.org/keywords/lpwan","display_name":"LPWAN","score":0.4964924454689026},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.3559379577636719},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3418040871620178},{"id":"https://openalex.org/keywords/internet-of-things","display_name":"Internet of Things","score":0.12357190251350403}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8082783222198486},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.7222544550895691},{"id":"https://openalex.org/C167900197","wikidata":"https://www.wikidata.org/wiki/Q11081100","display_name":"Spoofing attack","level":2,"score":0.653061032295227},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6402732729911804},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6143151521682739},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.565245509147644},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5286641120910645},{"id":"https://openalex.org/C2778403875","wikidata":"https://www.wikidata.org/wiki/Q20312394","display_name":"Adversarial machine learning","level":3,"score":0.5246827006340027},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5206750631332397},{"id":"https://openalex.org/C2776445043","wikidata":"https://www.wikidata.org/wiki/Q20706829","display_name":"LPWAN","level":3,"score":0.4964924454689026},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.3559379577636719},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3418040871620178},{"id":"https://openalex.org/C81860439","wikidata":"https://www.wikidata.org/wiki/Q251212","display_name":"Internet of Things","level":2,"score":0.12357190251350403},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/milcom58377.2023.10356224","is_oa":false,"landing_page_url":"https://doi.org/10.1109/milcom58377.2023.10356224","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"MILCOM 2023 - 2023 IEEE Military Communications Conference (MILCOM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1945616565","https://openalex.org/W2597831797","https://openalex.org/W2603396821","https://openalex.org/W2734885106","https://openalex.org/W2888024549","https://openalex.org/W2908164481","https://openalex.org/W2963047971","https://openalex.org/W2971661335","https://openalex.org/W2986517689","https://openalex.org/W2996613410","https://openalex.org/W3002574533","https://openalex.org/W3009482001","https://openalex.org/W3043963370","https://openalex.org/W3172135703","https://openalex.org/W3176020692","https://openalex.org/W3181667991","https://openalex.org/W3187344775","https://openalex.org/W3192729241","https://openalex.org/W3206629225","https://openalex.org/W3213282580","https://openalex.org/W4200018482","https://openalex.org/W4295308277","https://openalex.org/W6631190155","https://openalex.org/W6640425456","https://openalex.org/W6697523151","https://openalex.org/W6773217409"],"related_works":["https://openalex.org/W2888892063","https://openalex.org/W2740439328","https://openalex.org/W3088070213","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3103566983","https://openalex.org/W3167935049","https://openalex.org/W3029198973"],"abstract_inverted_index":{"Low-Power":[0],"Wide-Area":[1],"Network":[2],"(LPWAN)":[3],"technologies,":[4],"such":[5],"as":[6,191],"LoRa,":[7],"have":[8],"gained":[9],"significant":[10],"attention":[11],"for":[12,20,92,101,130],"their":[13],"ability":[14],"to":[15,58,199,204],"enable":[16],"long-range,":[17],"low-power":[18],"communication":[19],"Internet":[21],"of":[22,29,44,115,141,149,180,182],"Things":[23],"(IoT)":[24],"applications.":[25],"However,":[26],"the":[27,147,178,197],"security":[28],"LoRa":[30,63,70,102,131,187],"networks":[31],"remains":[32],"a":[33,54,80,192],"major":[34,193],"concern,":[35],"particularly":[36],"in":[37,174],"scenarios":[38],"where":[39],"device":[40,64,71,153,159],"identification":[41,65,154],"and":[42,46,66,155,195],"classification":[43,72,133,170,189],"legitimate":[45,67,156],"spoofed":[47],"signals":[48,106,117],"are":[49,107,128],"crucial.":[50],"This":[51],"paper":[52,176],"studies":[53],"deep":[55,75,136],"learning":[56,137],"framework":[57],"address":[59],"these":[60,142,168],"challenges,":[61],"considering":[62],"vs.":[68,157],"rogue":[69,105,119,158],"tasks.":[73,171],"A":[74],"neural":[76,82,87],"network":[77,83,88],"(DNN),":[78],"either":[79],"convolutional":[81],"(CNN)":[84],"or":[85,164],"feedforward":[86],"(FNN),":[89],"is":[90,144],"trained":[91],"each":[93],"task":[94],"by":[95,109,118,161],"utilizing":[96,162],"real":[97],"experimental":[98],"I/Q":[99],"data":[100],"signals,":[103],"while":[104],"generated":[108],"using":[110,135],"kernel":[111],"density":[112],"estimation":[113],"(KDE)":[114],"received":[116],"devices.":[120],"Fast":[121],"Gradient":[122],"Sign":[123],"Method":[124],"(FGSM)-based":[125],"adversarial":[126,183,205],"attacks":[127,143,184],"considered":[129],"signal":[132,169,188],"tasks":[134,190],"models.":[138],"The":[139],"impact":[140],"assessed":[145],"on":[146,185],"performance":[148],"two":[150],"tasks,":[151],"namely":[152],"classification,":[160],"separate":[163],"common":[165],"perturbations":[166],"against":[167],"Results":[172],"presented":[173],"this":[175],"quantify":[177],"level":[179],"transferability":[181],"different":[186],"vulnerability":[194],"highlight":[196],"need":[198],"make":[200],"IoT":[201],"applications":[202],"robust":[203],"attacks.":[206]},"counts_by_year":[{"year":2025,"cited_by_count":5}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}