{"id":"https://openalex.org/W4382395859","doi":"https://doi.org/10.1145/3586209.3591398","title":"Exploring Adversarial Attacks on Learning-based Localization","display_name":"Exploring Adversarial Attacks on Learning-based Localization","publication_year":2023,"publication_date":"2023-06-01","ids":{"openalex":"https://openalex.org/W4382395859","doi":"https://doi.org/10.1145/3586209.3591398"},"language":"en","primary_location":{"id":"doi:10.1145/3586209.3591398","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3586209.3591398","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3586209.3591398","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 ACM Workshop on Wireless Security and Machine Learning","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3586209.3591398","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5046494066","display_name":"Frost Mitchell","orcid":"https://orcid.org/0000-0002-2542-5836"},"institutions":[{"id":"https://openalex.org/I223532165","display_name":"University of Utah","ror":"https://ror.org/03r0ha626","country_code":"US","type":"education","lineage":["https://openalex.org/I223532165"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Frost Mitchell","raw_affiliation_strings":["University of Utah, Salt Lake City, UT, USA"],"raw_orcid":"https://orcid.org/0000-0002-2542-5836","affiliations":[{"raw_affiliation_string":"University of Utah, Salt Lake City, UT, USA","institution_ids":["https://openalex.org/I223532165"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103161524","display_name":"Phillip Smith","orcid":"https://orcid.org/0000-0002-3558-6027"},"institutions":[{"id":"https://openalex.org/I223532165","display_name":"University of Utah","ror":"https://ror.org/03r0ha626","country_code":"US","type":"education","lineage":["https://openalex.org/I223532165"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Phillip Smith","raw_affiliation_strings":["University of Utah, Salt Lake City, UT, USA"],"raw_orcid":"https://orcid.org/0000-0002-3558-6027","affiliations":[{"raw_affiliation_string":"University of Utah, Salt Lake City, UT, USA","institution_ids":["https://openalex.org/I223532165"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014414126","display_name":"Aditya Bhaskara","orcid":"https://orcid.org/0000-0001-5505-3140"},"institutions":[{"id":"https://openalex.org/I223532165","display_name":"University of Utah","ror":"https://ror.org/03r0ha626","country_code":"US","type":"education","lineage":["https://openalex.org/I223532165"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Aditya Bhaskara","raw_affiliation_strings":["University of Utah, Salt Lake City, UT, USA"],"raw_orcid":"https://orcid.org/0000-0001-5505-3140","affiliations":[{"raw_affiliation_string":"University of Utah, Salt Lake City, UT, USA","institution_ids":["https://openalex.org/I223532165"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5000720324","display_name":"Sneha Kumar Kasera","orcid":"https://orcid.org/0000-0002-5589-748X"},"institutions":[{"id":"https://openalex.org/I223532165","display_name":"University of Utah","ror":"https://ror.org/03r0ha626","country_code":"US","type":"education","lineage":["https://openalex.org/I223532165"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sneha Kumar Kasera","raw_affiliation_strings":["University of Utah, Salt Lake City, UT, USA"],"raw_orcid":"https://orcid.org/0000-0002-5589-748X","affiliations":[{"raw_affiliation_string":"University of Utah, Salt Lake City, UT, USA","institution_ids":["https://openalex.org/I223532165"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I223532165"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":5,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"15","last_page":"20"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9997000098228455,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","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/T12131","display_name":"Wireless Signal Modulation Classification","score":0.9986000061035156,"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/T10891","display_name":"Radar Systems and Signal Processing","score":0.9966999888420105,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.9054685831069946},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.813431978225708},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.7904540300369263},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.695283055305481},{"id":"https://openalex.org/keywords/rss","display_name":"RSS","score":0.6192225813865662},{"id":"https://openalex.org/keywords/adversarial-machine-learning","display_name":"Adversarial machine learning","score":0.6087064743041992},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6041218042373657},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.5899625420570374},{"id":"https://openalex.org/keywords/transmitter","display_name":"Transmitter","score":0.5011746883392334},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4846513867378235},{"id":"https://openalex.org/keywords/signal-strength","display_name":"Signal strength","score":0.4841518998146057},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.46111157536506653},{"id":"https://openalex.org/keywords/wireless-sensor-network","display_name":"Wireless sensor network","score":0.41777247190475464},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.35422277450561523},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3481861650943756},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.15278446674346924}],"concepts":[{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.9054685831069946},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.813431978225708},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7904540300369263},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.695283055305481},{"id":"https://openalex.org/C2385561","wikidata":"https://www.wikidata.org/wiki/Q45432","display_name":"RSS","level":2,"score":0.6192225813865662},{"id":"https://openalex.org/C2778403875","wikidata":"https://www.wikidata.org/wiki/Q20312394","display_name":"Adversarial machine learning","level":3,"score":0.6087064743041992},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6041218042373657},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.5899625420570374},{"id":"https://openalex.org/C47798520","wikidata":"https://www.wikidata.org/wiki/Q190157","display_name":"Transmitter","level":3,"score":0.5011746883392334},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4846513867378235},{"id":"https://openalex.org/C176808163","wikidata":"https://www.wikidata.org/wiki/Q17105794","display_name":"Signal strength","level":3,"score":0.4841518998146057},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.46111157536506653},{"id":"https://openalex.org/C24590314","wikidata":"https://www.wikidata.org/wiki/Q336038","display_name":"Wireless sensor network","level":2,"score":0.41777247190475464},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.35422277450561523},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3481861650943756},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.15278446674346924},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","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/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"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/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3586209.3591398","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3586209.3591398","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3586209.3591398","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 ACM Workshop on Wireless Security and Machine Learning","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3586209.3591398","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3586209.3591398","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3586209.3591398","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 ACM Workshop on Wireless Security and Machine Learning","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3675834352","display_name":null,"funder_award_id":"1827940, 1564287","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G71771288","display_name":null,"funder_award_id":"CNS-1827940","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7499104671","display_name":"PAWR Platform POWDER-RENEW: A Platform for Open Wireless Data-driven Experimental Research with Massive MIMO Capabilities","funder_award_id":"1827940","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8942888148","display_name":"NeTS: Medium: Collaborative Research: Detecting and Localizing Spectrum Offenders Using Crowdsourcing","funder_award_id":"1564287","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4382395859.pdf","grobid_xml":"https://content.openalex.org/works/W4382395859.grobid-xml"},"referenced_works_count":18,"referenced_works":["https://openalex.org/W2115991477","https://openalex.org/W2603766943","https://openalex.org/W2763254198","https://openalex.org/W2807007689","https://openalex.org/W2899088413","https://openalex.org/W2996904338","https://openalex.org/W3040002795","https://openalex.org/W3090321865","https://openalex.org/W3091795725","https://openalex.org/W3130402245","https://openalex.org/W3177746515","https://openalex.org/W3195387997","https://openalex.org/W3213747682","https://openalex.org/W4220823820","https://openalex.org/W4224933831","https://openalex.org/W4229371666","https://openalex.org/W4295308277","https://openalex.org/W4393923582"],"related_works":["https://openalex.org/W2162859609","https://openalex.org/W150547863","https://openalex.org/W2022445516","https://openalex.org/W3048732067","https://openalex.org/W4200318234","https://openalex.org/W4383468834","https://openalex.org/W2982532306","https://openalex.org/W1891938465","https://openalex.org/W1639914594","https://openalex.org/W4384648009"],"abstract_inverted_index":{"We":[0,33,55,72],"investigate":[1],"the":[2,15,31,35,82],"robustness":[3],"of":[4,17,111,117],"a":[5,52,115],"convolutional":[6],"neural":[7],"network":[8],"(CNN)":[9],"RF":[10],"transmitter":[11,39],"localization":[12],"model":[13],"in":[14,106],"face":[16],"adversarial":[18,85,121,129],"actors":[19],"which":[20],"may":[21],"poison":[22],"or":[23,29],"spoof":[24],"sensor":[25,43],"data":[26],"to":[27,37,63,87,94,104],"disrupt":[28],"defeat":[30],"algorithm.":[32],"train":[34,81],"CNN":[36,83],"estimate":[38],"locations":[40],"based":[41,75],"on":[42,76],"coordinates":[44],"and":[45,68,80,99,120],"received":[46],"signal":[47],"strength":[48],"(RSS)":[49],"measurements":[50],"from":[51,58],"real-world":[53],"dataset.":[54],"consider":[56],"attacks":[57,67,86,98],"adversaries":[59],"with":[60],"varying":[61],"capabilities":[62],"include":[64],"naive,":[65],"random":[66],"omniscient,":[69],"worst-case":[70],"attacks.":[71,130],"apply":[73],"countermeasures":[74,112],"statistical":[77,118],"outlier":[78],"approaches":[79],"against":[84,128],"improve":[88,100],"performance.":[89],"Adversarial":[90],"training":[91,122],"is":[92],"shown":[93],"completely":[95],"neutralize":[96],"some":[97],"accuracy":[101],"by":[102],"up":[103],"65%":[105],"other":[107],"cases.":[108],"Our":[109],"evaluation":[110],"indicates":[113],"that":[114],"combination":[116],"techniques":[119],"can":[123],"provide":[124],"more":[125],"robust":[126],"defense":[127]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
