{"id":"https://openalex.org/W7123353640","doi":"https://doi.org/10.1109/tce.2026.3652563","title":"QFedPhish: A Quantum-Inspired Federated Learning Approach for Detecting AI-Generated Phishing Attacks","display_name":"QFedPhish: A Quantum-Inspired Federated Learning Approach for Detecting AI-Generated Phishing Attacks","publication_year":2026,"publication_date":"2026-01-12","ids":{"openalex":"https://openalex.org/W7123353640","doi":"https://doi.org/10.1109/tce.2026.3652563"},"language":null,"primary_location":{"id":"doi:10.1109/tce.2026.3652563","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tce.2026.3652563","pdf_url":null,"source":{"id":"https://openalex.org/S126824455","display_name":"IEEE Transactions on Consumer Electronics","issn_l":"0098-3063","issn":["0098-3063","1558-4127"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Consumer Electronics","raw_type":"journal-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/A5122860173","display_name":"Abdul Malik","orcid":null},"institutions":[{"id":"https://openalex.org/I2802999120","display_name":"Government of Khyber Pakhtunkhwa","ror":"https://ror.org/04zyfmb02","country_code":"PK","type":"government","lineage":["https://openalex.org/I2802999120"]}],"countries":["PK"],"is_corresponding":true,"raw_author_name":"Abdul Malik","raw_affiliation_strings":["Higher Education Department, Government College of Management Sciences Timergara, Timergara, Khyber Pakhtunkhwa, Pakistan"],"affiliations":[{"raw_affiliation_string":"Higher Education Department, Government College of Management Sciences Timergara, Timergara, Khyber Pakhtunkhwa, Pakistan","institution_ids":["https://openalex.org/I2802999120"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5015732743","display_name":"Saeed Mian Qaisar","orcid":"https://orcid.org/0000-0002-4268-3482"},"institutions":[{"id":"https://openalex.org/I2803079837","display_name":"American University of the Middle East","ror":"https://ror.org/02gqgne03","country_code":"KW","type":"education","lineage":["https://openalex.org/I2803079837"]}],"countries":["KW"],"is_corresponding":false,"raw_author_name":"Saeed Mian Qaisar","raw_affiliation_strings":["College of Engineering and Technology, American University of the Middle East, Egaila, Kuwait"],"affiliations":[{"raw_affiliation_string":"College of Engineering and Technology, American University of the Middle East, Egaila, Kuwait","institution_ids":["https://openalex.org/I2803079837"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5122860173"],"corresponding_institution_ids":["https://openalex.org/I2802999120"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0660918,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"72","issue":"1","first_page":"211","last_page":"222"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11644","display_name":"Spam and Phishing Detection","score":0.8813999891281128,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T11644","display_name":"Spam and Phishing Detection","score":0.8813999891281128,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T11241","display_name":"Advanced Malware Detection Techniques","score":0.029200000688433647,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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":0.012199999764561653,"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/phishing","display_name":"Phishing","score":0.8033999800682068},{"id":"https://openalex.org/keywords/differential-privacy","display_name":"Differential privacy","score":0.4948999881744385},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.4787999987602234},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.40610000491142273},{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.3944999873638153},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.3723999857902527},{"id":"https://openalex.org/keywords/personalization","display_name":"Personalization","score":0.3582000136375427},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.35339999198913574},{"id":"https://openalex.org/keywords/malware","display_name":"Malware","score":0.3452000021934509}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.833899974822998},{"id":"https://openalex.org/C83860907","wikidata":"https://www.wikidata.org/wiki/Q135005","display_name":"Phishing","level":3,"score":0.8033999800682068},{"id":"https://openalex.org/C23130292","wikidata":"https://www.wikidata.org/wiki/Q5275358","display_name":"Differential privacy","level":2,"score":0.4948999881744385},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.4787999987602234},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.40610000491142273},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.39629998803138733},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.3944999873638153},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38850000500679016},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3723999857902527},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.37070000171661377},{"id":"https://openalex.org/C183003079","wikidata":"https://www.wikidata.org/wiki/Q1000371","display_name":"Personalization","level":2,"score":0.3582000136375427},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.35339999198913574},{"id":"https://openalex.org/C541664917","wikidata":"https://www.wikidata.org/wiki/Q14001","display_name":"Malware","level":2,"score":0.3452000021934509},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.3425999879837036},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.3375999927520752},{"id":"https://openalex.org/C2779585090","wikidata":"https://www.wikidata.org/wiki/Q3457762","display_name":"Resilience (materials science)","level":2,"score":0.32919999957084656},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.32749998569488525},{"id":"https://openalex.org/C10511746","wikidata":"https://www.wikidata.org/wiki/Q899388","display_name":"Data security","level":3,"score":0.31610000133514404},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.3149999976158142},{"id":"https://openalex.org/C123201435","wikidata":"https://www.wikidata.org/wiki/Q456632","display_name":"Information privacy","level":2,"score":0.31299999356269836},{"id":"https://openalex.org/C137822555","wikidata":"https://www.wikidata.org/wiki/Q2587068","display_name":"Information sensitivity","level":2,"score":0.303600013256073},{"id":"https://openalex.org/C110875604","wikidata":"https://www.wikidata.org/wiki/Q75","display_name":"The Internet","level":2,"score":0.2994999885559082},{"id":"https://openalex.org/C148730421","wikidata":"https://www.wikidata.org/wiki/Q141090","display_name":"Encryption","level":2,"score":0.29030001163482666},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2849000096321106},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.2689000070095062},{"id":"https://openalex.org/C93996380","wikidata":"https://www.wikidata.org/wiki/Q44127","display_name":"Server","level":2,"score":0.2685999870300293},{"id":"https://openalex.org/C2779201187","wikidata":"https://www.wikidata.org/wiki/Q2775060","display_name":"Information leakage","level":2,"score":0.2671000063419342},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.2524999976158142}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tce.2026.3652563","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tce.2026.3652563","pdf_url":null,"source":{"id":"https://openalex.org/S126824455","display_name":"IEEE Transactions on Consumer Electronics","issn_l":"0098-3063","issn":["0098-3063","1558-4127"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Consumer Electronics","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.5132483839988708,"id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Advancements":[0],"in":[1,36,44,183,186,189,192],"artificial":[2],"intelligence":[3],"(AI)":[4],"offer":[5],"opportunities":[6],"but":[7],"have":[8],"also":[9],"contributed":[10],"to":[11,63,136],"the":[12,18,32,130,175],"emergence":[13],"of":[14,22,72,162,181],"AIgenerated":[15],"attacks,":[16],"escalating":[17],"complexity":[19],"and":[20,42,65,104,110,155,165,201],"scale":[21],"modern":[23,45],"cyber":[24],"threats.":[25],"These":[26],"attacks":[27],"generate":[28],"serious":[29],"concerns":[30],"for":[31],"traditional":[33],"security":[34],"systems":[35],"maintaining":[37],"accuracy,":[38,184],"privacy,":[39],"false-positive":[40],"control,":[41],"resilience":[43],"distributed":[46],"environments.":[47],"To":[48],"cope":[49],"with":[50,76,152,195],"these":[51],"concerns,":[52],"this":[53],"paper":[54],"proposes":[55],"QFedPhish,":[56],"a":[57,87,96,126],"quantum-inspired":[58,97],"federated":[59,149],"learning":[60,150],"approach":[61],"designed":[62],"identify":[64],"detect":[66],"AI-generated":[67],"phishing":[68,74,82,115],"attacks.":[69,167],"A":[70],"combination":[71],"real-world":[73],"logs":[75],"synthetically":[77],"generated":[78],"large":[79],"language":[80],"model-based":[81],"attempts":[83],"is":[84,92],"built":[85],"as":[86],"representative":[88],"dataset.":[89],"This":[90],"data":[91,163],"then":[93],"transformed":[94],"through":[95],"feature":[98],"encoder,":[99],"where":[100],"random":[101],"Fourier":[102],"features":[103],"tensor-network":[105],"compression":[106],"capture":[107],"subtle":[108],"stylometric":[109],"structural":[111],"cues":[112],"that":[113,171],"distinguish":[114],"content":[116],"from":[117],"legitimate":[118],"content.":[119],"Next,":[120],"each":[121],"participating":[122],"client":[123],"locally":[124],"trains":[125],"lightweight":[127],"classifier":[128],"on":[129],"resulting":[131],"embeddings,":[132],"enabling":[133],"model":[134,144],"adaptation":[135],"heterogeneous":[137],"environments":[138],"without":[139],"exposing":[140],"raw":[141],"data.":[142],"Finally,":[143],"updates":[145],"are":[146],"aggregated":[147],"using":[148],"enhanced":[151],"differential":[153],"privacy":[154],"resilient":[156],"aggregation":[157],"mechanisms.":[158],"It":[159],"mitigates":[160],"risks":[161],"leakage":[164],"poisoning":[166],"Experimental":[168],"evaluation":[169],"demonstrates":[170],"QFedPhish":[172],"consistently":[173],"outperforms":[174],"best":[176],"benchmark":[177],"models,":[178],"achieving":[179],"gains":[180],"0.10%":[182],"0.33%":[185],"precision,":[187],"0.41%":[188],"F1-score,":[190],"0.61%":[191],"AUC,":[193],"along":[194],"reducing":[196],"communication":[197],"overhead":[198],"by":[199,205],"3.89%":[200],"improving":[202],"convergence":[203],"speed":[204],"3.66%.":[206]},"counts_by_year":[],"updated_date":"2026-03-26T06:05:38.182114","created_date":"2026-01-14T00:00:00"}