{"id":"https://openalex.org/W7125903493","doi":"https://doi.org/10.1109/smc58881.2025.11343314","title":"FLIFRA: Hybrid Data Poisoning Attack Detection in Federated Learning for IoT Security","display_name":"FLIFRA: Hybrid Data Poisoning Attack Detection in Federated Learning for IoT Security","publication_year":2025,"publication_date":"2025-10-05","ids":{"openalex":"https://openalex.org/W7125903493","doi":"https://doi.org/10.1109/smc58881.2025.11343314"},"language":"en","primary_location":{"id":"doi:10.1109/smc58881.2025.11343314","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc58881.2025.11343314","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://hdl.handle.net/2434/1173939","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5081131099","display_name":"Mulualem Bitew Anley","orcid":"https://orcid.org/0009-0001-1148-1247"},"institutions":[{"id":"https://openalex.org/I189158943","display_name":"University of Milan","ror":"https://ror.org/00wjc7c48","country_code":"IT","type":"education","lineage":["https://openalex.org/I189158943"]}],"countries":["IT"],"is_corresponding":true,"raw_author_name":"Mulualem Bitew Anley","raw_affiliation_strings":["Università Degli Studi di Milano,Department of Computer Science,Italy"],"affiliations":[{"raw_affiliation_string":"Università Degli Studi di Milano,Department of Computer Science,Italy","institution_ids":["https://openalex.org/I189158943"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031772222","display_name":"Angelo Genovese","orcid":"https://orcid.org/0000-0002-3683-4723"},"institutions":[{"id":"https://openalex.org/I189158943","display_name":"University of Milan","ror":"https://ror.org/00wjc7c48","country_code":"IT","type":"education","lineage":["https://openalex.org/I189158943"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Angelo Genovese","raw_affiliation_strings":["Università Degli Studi di Milano,Department of Computer Science,Italy"],"affiliations":[{"raw_affiliation_string":"Università Degli Studi di Milano,Department of Computer Science,Italy","institution_ids":["https://openalex.org/I189158943"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084905915","display_name":"Tibebe Beshah Tesema","orcid":"https://orcid.org/0000-0001-6418-0707"},"institutions":[{"id":"https://openalex.org/I4537092","display_name":"Addis Ababa University","ror":"https://ror.org/038b8e254","country_code":"ET","type":"education","lineage":["https://openalex.org/I4537092"]}],"countries":["ET"],"is_corresponding":false,"raw_author_name":"Tibebe Beshah Tesema","raw_affiliation_strings":["Addis Ababa University,School of Information Science,Ethiopia"],"affiliations":[{"raw_affiliation_string":"Addis Ababa University,School of Information Science,Ethiopia","institution_ids":["https://openalex.org/I4537092"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5094683197","display_name":"\uf0b7 Mr","orcid":null},"institutions":[{"id":"https://openalex.org/I189158943","display_name":"University of Milan","ror":"https://ror.org/00wjc7c48","country_code":"IT","type":"education","lineage":["https://openalex.org/I189158943"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Vincenzo Piuri","raw_affiliation_strings":["Università Degli Studi di Milano,Department of Computer Science,Italy"],"affiliations":[{"raw_affiliation_string":"Università Degli Studi di Milano,Department of Computer Science,Italy","institution_ids":["https://openalex.org/I189158943"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5081131099"],"corresponding_institution_ids":["https://openalex.org/I189158943"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.87128183,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"6816","last_page":"6823"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.4794999957084656,"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/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.4794999957084656,"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/T10400","display_name":"Network Security and Intrusion Detection","score":0.14509999752044678,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.11209999769926071,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.620199978351593},{"id":"https://openalex.org/keywords/compromise","display_name":"Compromise","score":0.5081999897956848},{"id":"https://openalex.org/keywords/data-aggregator","display_name":"Data aggregator","score":0.5023000240325928},{"id":"https://openalex.org/keywords/limiting","display_name":"Limiting","score":0.4959000051021576},{"id":"https://openalex.org/keywords/isolation","display_name":"Isolation (microbiology)","score":0.4462999999523163},{"id":"https://openalex.org/keywords/weighting","display_name":"Weighting","score":0.3984000086784363},{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.38519999384880066},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.3668999969959259},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.3605000078678131}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7778000235557556},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.620199978351593},{"id":"https://openalex.org/C46355384","wikidata":"https://www.wikidata.org/wiki/Q726686","display_name":"Compromise","level":2,"score":0.5081999897956848},{"id":"https://openalex.org/C82578977","wikidata":"https://www.wikidata.org/wiki/Q16773055","display_name":"Data aggregator","level":3,"score":0.5023000240325928},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.4959000051021576},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.4560999870300293},{"id":"https://openalex.org/C2775941552","wikidata":"https://www.wikidata.org/wiki/Q25212305","display_name":"Isolation (microbiology)","level":2,"score":0.4462999999523163},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.3984000086784363},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.38519999384880066},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3686999976634979},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3668999969959259},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3605000078678131},{"id":"https://openalex.org/C123201435","wikidata":"https://www.wikidata.org/wiki/Q456632","display_name":"Information privacy","level":2,"score":0.353300005197525},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.3395000100135803},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.323199987411499},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.3212999999523163},{"id":"https://openalex.org/C4679612","wikidata":"https://www.wikidata.org/wiki/Q866298","display_name":"Aggregate (composite)","level":2,"score":0.3086000084877014},{"id":"https://openalex.org/C81860439","wikidata":"https://www.wikidata.org/wiki/Q251212","display_name":"Internet of Things","level":2,"score":0.2964000105857849},{"id":"https://openalex.org/C23130292","wikidata":"https://www.wikidata.org/wiki/Q5275358","display_name":"Differential privacy","level":2,"score":0.2955999970436096},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.29339998960494995},{"id":"https://openalex.org/C33762810","wikidata":"https://www.wikidata.org/wiki/Q461671","display_name":"Data integrity","level":2,"score":0.28940001130104065},{"id":"https://openalex.org/C133462117","wikidata":"https://www.wikidata.org/wiki/Q4929239","display_name":"Data collection","level":2,"score":0.2800000011920929},{"id":"https://openalex.org/C10511746","wikidata":"https://www.wikidata.org/wiki/Q899388","display_name":"Data security","level":3,"score":0.27810001373291016},{"id":"https://openalex.org/C35525427","wikidata":"https://www.wikidata.org/wiki/Q745881","display_name":"Intrusion detection system","level":2,"score":0.27559998631477356},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.2651999890804291},{"id":"https://openalex.org/C70061542","wikidata":"https://www.wikidata.org/wiki/Q989016","display_name":"Distributed database","level":2,"score":0.25949999690055847}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/smc58881.2025.11343314","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc58881.2025.11343314","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","raw_type":"proceedings-article"},{"id":"pmh:oai:air.unimi.it:2434/1173939","is_oa":true,"landing_page_url":"https://hdl.handle.net/2434/1173939","pdf_url":null,"source":{"id":"https://openalex.org/S4306400516","display_name":"Archivio Istituzionale della Ricerca (Universita Degli Studi Di Milano)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I189158943","host_organization_name":"University of Milan","host_organization_lineage":["https://openalex.org/I189158943"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"info:eu-repo/semantics/bookPart"}],"best_oa_location":{"id":"pmh:oai:air.unimi.it:2434/1173939","is_oa":true,"landing_page_url":"https://hdl.handle.net/2434/1173939","pdf_url":null,"source":{"id":"https://openalex.org/S4306400516","display_name":"Archivio Istituzionale della Ricerca (Universita Degli Studi Di Milano)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I189158943","host_organization_name":"University of Milan","host_organization_lineage":["https://openalex.org/I189158943"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"info:eu-repo/semantics/bookPart"},"sustainable_development_goals":[{"score":0.4566687345504761,"display_name":"Life in Land","id":"https://metadata.un.org/sdg/15"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W2789828921","https://openalex.org/W2963748489","https://openalex.org/W3087391814","https://openalex.org/W3141567114","https://openalex.org/W4295806247","https://openalex.org/W4386804452","https://openalex.org/W4388591491","https://openalex.org/W4391724779","https://openalex.org/W4398161342","https://openalex.org/W4399915793","https://openalex.org/W4401752304","https://openalex.org/W4404437806","https://openalex.org/W4415623556"],"related_works":[],"abstract_inverted_index":{"The":[0],"rapid":[1],"expansion":[2],"of":[3,144,155,172],"IoT":[4,36],"devices":[5],"has":[6],"transformed":[7],"numerous":[8],"industries":[9],"by":[10,58],"enabling":[11],"extensive":[12],"data":[13,29,200],"collection":[14],"and":[15,134,150,160,178,193],"real-time":[16],"analytics.":[17],"Federated":[18],"Learning":[19,98],"(FL)":[20],"offers":[21],"a":[22,104],"decentralized":[23],"model":[24,49,57,69],"training":[25],"paradigm":[26],"that":[27,45,108,163],"ensures":[28],"privacy,":[30],"making":[31],"it":[32,39],"particularly":[33],"suitable":[34],"for":[35],"environments.":[37,201],"Yet,":[38],"remains":[40],"vulnerable":[41],"to":[42,73,138],"poisoning":[43,156],"attacks":[44],"can":[46],"severely":[47],"compromise":[48,54],"integrity,":[50],"wherein":[51],"malicious":[52,130],"clients":[53],"the":[55,123,164,168,181],"global":[56,68,80],"injecting":[59],"poisoned":[60],"updates.":[61],"Existing":[62],"defenses,":[63],"which":[64],"focus":[65],"primarily":[66],"on":[67],"performance,":[70],"often":[71],"fail":[72],"effectively":[74],"integrate":[75],"local":[76],"anomaly":[77,111],"detection":[78,112,189],"with":[79,101,117],"weighting":[81],"mechanisms,":[82],"thus":[83],"limiting":[84],"their":[85],"efficacy":[86],"against":[87],"such":[88],"threats.":[89],"Addressing":[90],"this":[91],"research":[92],"gap,":[93],"we":[94],"propose":[95],"FLIFRA":[96],"(Federated":[97],"Isolation":[99,114],"Forest":[100,115],"Robust":[102],"Aggregation),":[103],"hybrid":[105],"defense":[106],"framework":[107,186],"combines":[109],"client-side":[110],"using":[113],"(iForest)":[116],"dynamic":[118],"reputation-based":[119],"robust":[120],"aggregation":[121,133,170],"at":[122],"server.":[124],"This":[125],"dual-layer":[126],"approach":[127],"filters":[128],"out":[129],"updates":[131],"before":[132],"adjusts":[135],"client":[136],"reputations":[137],"mitigate":[139],"adversarial":[140],"influence.":[141],"Our":[142],"evaluation":[143],"three":[145],"cybersecurity":[146],"datasets":[147],"(CIC-IDS2018,":[148],"BoT-IoT,":[149],"UNSW-NB15)":[151],"under":[152],"various":[153],"intensities":[154],"(10%,":[157],"20%,":[158],"30%,":[159],"40%)":[161],"demonstrates":[162],"proposed":[165],"method":[166],"outperforms":[167],"traditional":[169],"schemes":[171],"FedAvg,":[173],"Krum,":[174],"Trimmed":[175],"Mean,":[176],"DRRA,":[177],"WeiDetect":[179],"in":[180,197],"literature.":[182],"In":[183],"particular,":[184],"our":[185],"achieves":[187],"higher":[188],"accuracy,":[190],"faster":[191],"convergence,":[192],"improved":[194],"stability,":[195],"even":[196],"highly":[198],"heterogeneous":[199]},"counts_by_year":[],"updated_date":"2026-01-29T23:17:01.242718","created_date":"2026-01-29T00:00:00"}