{"id":"https://openalex.org/W4402811558","doi":"https://doi.org/10.1109/csr61664.2024.10679472","title":"A Novel Approach for Securing Federated Learning: Detection and Defense Against Model Poisoning Attacks","display_name":"A Novel Approach for Securing Federated Learning: Detection and Defense Against Model Poisoning Attacks","publication_year":2024,"publication_date":"2024-09-02","ids":{"openalex":"https://openalex.org/W4402811558","doi":"https://doi.org/10.1109/csr61664.2024.10679472"},"language":"en","primary_location":{"id":"doi:10.1109/csr61664.2024.10679472","is_oa":false,"landing_page_url":"https://doi.org/10.1109/csr61664.2024.10679472","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Cyber Security and Resilience (CSR)","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/A5107494390","display_name":"Giovanni Maria Cristiano","orcid":null},"institutions":[{"id":"https://openalex.org/I183638586","display_name":"Parthenope University of Naples","ror":"https://ror.org/05pcv4v03","country_code":"IT","type":"education","lineage":["https://openalex.org/I183638586"]}],"countries":["IT"],"is_corresponding":true,"raw_author_name":"Giovanni Maria Cristiano","raw_affiliation_strings":["University of Naples &#x2018;Parthenope&#x2019; Centro Direzionale,Napoli,Italy,80133"],"affiliations":[{"raw_affiliation_string":"University of Naples &#x2018;Parthenope&#x2019; Centro Direzionale,Napoli,Italy,80133","institution_ids":["https://openalex.org/I183638586"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053058934","display_name":"Salvatore D\u2019Antonio","orcid":"https://orcid.org/0000-0001-9327-0138"},"institutions":[{"id":"https://openalex.org/I183638586","display_name":"Parthenope University of Naples","ror":"https://ror.org/05pcv4v03","country_code":"IT","type":"education","lineage":["https://openalex.org/I183638586"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Salvatore D'Antonio","raw_affiliation_strings":["University of Naples &#x2018;Parthenope&#x2019; Centro Direzionale,Napoli,Italy,80133"],"affiliations":[{"raw_affiliation_string":"University of Naples &#x2018;Parthenope&#x2019; Centro Direzionale,Napoli,Italy,80133","institution_ids":["https://openalex.org/I183638586"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5008002054","display_name":"Federica Uccello","orcid":"https://orcid.org/0000-0001-9243-7047"},"institutions":[{"id":"https://openalex.org/I183638586","display_name":"Parthenope University of Naples","ror":"https://ror.org/05pcv4v03","country_code":"IT","type":"education","lineage":["https://openalex.org/I183638586"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Federica Uccello","raw_affiliation_strings":["University of Naples &#x2018;Parthenope&#x2019; Centro Direzionale,Napoli,Italy,80133"],"affiliations":[{"raw_affiliation_string":"University of Naples &#x2018;Parthenope&#x2019; Centro Direzionale,Napoli,Italy,80133","institution_ids":["https://openalex.org/I183638586"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5107494390"],"corresponding_institution_ids":["https://openalex.org/I183638586"],"apc_list":null,"apc_paid":null,"fwci":0.7088,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.76205289,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"664","last_page":"669"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9810000061988831,"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":0.9810000061988831,"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/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9749000072479248,"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/T10237","display_name":"Cryptography and Data Security","score":0.9108999967575073,"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/computer-science","display_name":"Computer science","score":0.7449302673339844},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.6175020337104797},{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.49604281783103943},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.28314119577407837}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7449302673339844},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.6175020337104797},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.49604281783103943},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.28314119577407837}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/csr61664.2024.10679472","is_oa":false,"landing_page_url":"https://doi.org/10.1109/csr61664.2024.10679472","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Cyber Security and Resilience (CSR)","raw_type":"proceedings-article"},{"id":"pmh:oai:ricerca.uniparthenope.it:11367/140141","is_oa":false,"landing_page_url":"https://hdl.handle.net/11367/140141","pdf_url":null,"source":{"id":"https://openalex.org/S4377196432","display_name":"CINECA IRIS Institutial research information system (Parthenope University of Naples)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I183638586","host_organization_name":"Parthenope University of Naples","host_organization_lineage":["https://openalex.org/I183638586"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Gender equality","score":0.5799999833106995,"id":"https://metadata.un.org/sdg/5"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320324633","display_name":"Istituto Nazionale per l'Assicurazione Contro Gli Infortuni sul Lavoro","ror":"https://ror.org/01t264m74"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W2112796928","https://openalex.org/W2809684781","https://openalex.org/W2962949934","https://openalex.org/W3004155269","https://openalex.org/W3036791758","https://openalex.org/W3046653923","https://openalex.org/W3111919937","https://openalex.org/W3135347465","https://openalex.org/W3157850870","https://openalex.org/W4229455429","https://openalex.org/W4290948380","https://openalex.org/W4295806247","https://openalex.org/W4310494058","https://openalex.org/W4317795078","https://openalex.org/W6637131181","https://openalex.org/W6695838908","https://openalex.org/W6756840679","https://openalex.org/W6760425369","https://openalex.org/W6770634426","https://openalex.org/W6801469080","https://openalex.org/W6803524877"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W4298221930","https://openalex.org/W2390279801","https://openalex.org/W2777914285","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W3013363440"],"abstract_inverted_index":{"Federated":[0,106],"Learning":[1,107],"(FL)":[2],"holds":[3],"great":[4],"promise":[5],"for":[6,56],"collaborative":[7],"model":[8,19],"training":[9],"across":[10],"distributed":[11],"devices.":[12],"However,":[13],"it":[14],"faces":[15],"a":[16,39,78],"significant":[17,40],"threat:":[18],"poisoning":[20],"attacks.":[21,113],"In":[22],"particular,":[23],"Byzantine":[24],"attacks":[25],"can":[26],"severely":[27],"compromise":[28],"the":[29,46,53,91,103],"accuracy":[30,44],"of":[31,48,94,105],"FL":[32,83],"systems.":[33],"Through":[34],"experimental":[35],"analysis,":[36],"we":[37],"demonstrate":[38],"degradation":[41],"in":[42,71],"network":[43],"as":[45],"percentage":[47],"malicious":[49],"participants":[50],"increases,":[51],"underscoring":[52],"critical":[54,92],"need":[55],"robust":[57,96],"defense":[58],"mechanisms.":[59],"Our":[60],"proposed":[61],"detection":[62,97],"strategy,":[63],"based":[64],"on":[65],"clustering":[66],"algorithms,":[67],"exhibits":[68],"promising":[69],"results":[70],"identifying":[72],"outliers":[73],"and":[74,98,111],"potential":[75],"attackers,":[76],"offering":[77],"proactive":[79],"approach":[80],"to":[81,101],"safeguarding":[82],"systems":[84],"against":[85,108],"adversarial":[86],"manipulation.":[87],"This":[88],"work":[89],"underscores":[90],"necessity":[93],"implementing":[95],"mitigation":[99],"strategies":[100],"improve":[102],"resilience":[104],"increasingly":[109],"sophisticated":[110],"pervasive":[112]},"counts_by_year":[{"year":2025,"cited_by_count":2}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2024-09-25T00:00:00"}
