{"id":"https://openalex.org/W4414539546","doi":"https://doi.org/10.1109/icc52391.2025.11161334","title":"A Transfer Learning Framework for Anomaly Detection in Multivariate IoT Traffic Data","display_name":"A Transfer Learning Framework for Anomaly Detection in Multivariate IoT Traffic Data","publication_year":2025,"publication_date":"2025-06-08","ids":{"openalex":"https://openalex.org/W4414539546","doi":"https://doi.org/10.1109/icc52391.2025.11161334"},"language":"en","primary_location":{"id":"doi:10.1109/icc52391.2025.11161334","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icc52391.2025.11161334","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICC 2025 - IEEE International Conference on Communications","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/A5116054010","display_name":"Mahshid Rezakhani","orcid":null},"institutions":[{"id":"https://openalex.org/I8078737","display_name":"Clemson University","ror":"https://ror.org/037s24f05","country_code":"US","type":"education","lineage":["https://openalex.org/I8078737"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mahshid Rezakhani","raw_affiliation_strings":["Clemson University,Holcombe Department of Electrical and Computer Engineering,Clemson,SC,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Clemson University,Holcombe Department of Electrical and Computer Engineering,Clemson,SC,USA","institution_ids":["https://openalex.org/I8078737"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083576650","display_name":"Tolunay Seyfi","orcid":"https://orcid.org/0000-0003-0633-9593"},"institutions":[{"id":"https://openalex.org/I8078737","display_name":"Clemson University","ror":"https://ror.org/037s24f05","country_code":"US","type":"education","lineage":["https://openalex.org/I8078737"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tolunay Seyfi","raw_affiliation_strings":["Clemson University,Holcombe Department of Electrical and Computer Engineering,Clemson,SC,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Clemson University,Holcombe Department of Electrical and Computer Engineering,Clemson,SC,USA","institution_ids":["https://openalex.org/I8078737"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5035395012","display_name":"Fatemeh Afghah","orcid":"https://orcid.org/0000-0002-2315-1173"},"institutions":[{"id":"https://openalex.org/I8078737","display_name":"Clemson University","ror":"https://ror.org/037s24f05","country_code":"US","type":"education","lineage":["https://openalex.org/I8078737"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Fatemeh Afghah","raw_affiliation_strings":["Clemson University,Holcombe Department of Electrical and Computer Engineering,Clemson,SC,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Clemson University,Holcombe Department of Electrical and Computer Engineering,Clemson,SC,USA","institution_ids":["https://openalex.org/I8078737"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I8078737"],"apc_list":null,"apc_paid":null,"fwci":15.851,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.99275685,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"4975","last_page":"4980"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10400","display_name":"Network Security and Intrusion Detection","score":0.9994000196456909,"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":0.9994000196456909,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9994000196456909,"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/T10876","display_name":"Fault Detection and Control Systems","score":0.9717000126838684,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/anomaly-detection","display_name":"Anomaly detection","score":0.7926999926567078},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.6521000266075134},{"id":"https://openalex.org/keywords/intrusion-detection-system","display_name":"Intrusion detection system","score":0.5623999834060669},{"id":"https://openalex.org/keywords/labeled-data","display_name":"Labeled data","score":0.5066999793052673},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.4941999912261963},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.42649999260902405},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.3831999897956848},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.37700000405311584}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.7926999926567078},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7656000256538391},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.6521000266075134},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5758000016212463},{"id":"https://openalex.org/C35525427","wikidata":"https://www.wikidata.org/wiki/Q745881","display_name":"Intrusion detection system","level":2,"score":0.5623999834060669},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5526999831199646},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.5066999793052673},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.4941999912261963},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.46549999713897705},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.42649999260902405},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.3831999897956848},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.37700000405311584},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.367000013589859},{"id":"https://openalex.org/C58973888","wikidata":"https://www.wikidata.org/wiki/Q1041418","display_name":"Semi-supervised learning","level":2,"score":0.36039999127388},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.3100000023841858},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.299699991941452},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.2849999964237213},{"id":"https://openalex.org/C63969886","wikidata":"https://www.wikidata.org/wiki/Q3536440","display_name":"Internet traffic","level":3,"score":0.27459999918937683},{"id":"https://openalex.org/C182590292","wikidata":"https://www.wikidata.org/wiki/Q989632","display_name":"Network security","level":2,"score":0.2728999853134155},{"id":"https://openalex.org/C137524506","wikidata":"https://www.wikidata.org/wiki/Q2247688","display_name":"Anomaly-based intrusion detection system","level":3,"score":0.27079999446868896},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.2703999876976013},{"id":"https://openalex.org/C105002631","wikidata":"https://www.wikidata.org/wiki/Q4833645","display_name":"Subject-matter expert","level":3,"score":0.26579999923706055},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.26260000467300415},{"id":"https://openalex.org/C110875604","wikidata":"https://www.wikidata.org/wiki/Q75","display_name":"The Internet","level":2,"score":0.25870001316070557}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icc52391.2025.11161334","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icc52391.2025.11161334","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICC 2025 - IEEE International Conference on Communications","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1193190396","display_name":null,"funder_award_id":"CNS- 2318726,CNS-2232048","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W2940895343","https://openalex.org/W3004207920","https://openalex.org/W3007023507","https://openalex.org/W3012082538","https://openalex.org/W3015959599","https://openalex.org/W3048529935","https://openalex.org/W3153970166","https://openalex.org/W4310699669","https://openalex.org/W4401337939"],"related_works":[],"abstract_inverted_index":{"In":[0],"recent":[1],"years,":[2],"rapid":[3],"technological":[4],"advancements":[5],"and":[6,21,38,99],"expanded":[7],"Internet":[8],"access":[9],"have":[10,47],"led":[11],"to":[12,122],"a":[13,112,119,137,153],"significant":[14],"rise":[15],"in":[16,50,73,85,125,160,174,195],"anomalies":[17,124,198],"within":[18,199],"network":[19],"traffic":[20],"time-series":[22,86,162],"data.":[23,69],"Prompt":[24],"detection":[25,84,159,186],"of":[26,66,79,94,140],"these":[27,149],"irregularities":[28],"is":[29,59,76],"crucial":[30],"for":[31,54,82,157],"ensuring":[32],"service":[33],"quality,":[34],"preventing":[35],"financial":[36],"losses,":[37],"maintaining":[39],"robust":[40],"security":[41],"standards.":[42],"While":[43],"machine":[44,97],"learning":[45,98,102,109],"algorithms":[46],"shown":[48],"promise":[49],"achieving":[51],"high":[52],"accuracy":[53],"anomaly":[55,83,158],"detection,":[56],"their":[57,67],"performance":[58],"often":[60],"constrained":[61],"by":[62],"the":[63,77,91,144,176],"specific":[64],"conditions":[65],"training":[68,92],"A":[70],"persistent":[71],"challenge":[72],"this":[74],"domain":[75,121],"scarcity":[78],"labeled":[80,141,172],"data":[81,117,142,173],"datasets.":[87,163],"This":[88],"limitation":[89],"hampers":[90],"efficacy":[93],"both":[95],"traditional":[96],"advanced":[100],"deep":[101],"models.":[103],"To":[104,147],"address":[105],"this,":[106],"unsupervised":[107],"transfer":[108,154],"emerges":[110],"as":[111],"viable":[113],"solution,":[114],"leveraging":[115],"unlabeled":[116,127,202],"from":[118,143],"source":[120,177],"identify":[123],"an":[126,200],"target":[128,145,179,203],"domain.":[129,146,204],"However,":[130],"many":[131],"existing":[132,193],"approaches":[133],"still":[134],"depend":[135],"on":[136,183],"small":[138],"amount":[139],"overcome":[148],"constraints,":[150],"we":[151],"propose":[152],"learning-based":[155],"model":[156,191],"multivariate":[161],"Unlike":[164],"conventional":[165],"methods,":[166],"our":[167,190],"approach":[168],"does":[169],"not":[170],"require":[171],"either":[175],"or":[178],"domains.":[180],"Empirical":[181],"evaluations":[182],"novel":[184],"intrusion":[185],"datasets":[187],"demonstrate":[188],"that":[189],"outperforms":[192],"techniques":[194],"accurately":[196],"identifying":[197],"entirely":[201]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":6}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}