{"id":"https://openalex.org/W7133242476","doi":"https://doi.org/10.1109/sds68531.2025.11409694","title":"Improved Detection and Prevention of Phishing URLs Using Ensemble Machine Learning Techniques","display_name":"Improved Detection and Prevention of Phishing URLs Using Ensemble Machine Learning Techniques","publication_year":2025,"publication_date":"2025-12-02","ids":{"openalex":"https://openalex.org/W7133242476","doi":"https://doi.org/10.1109/sds68531.2025.11409694"},"language":"en","primary_location":{"id":"doi:10.1109/sds68531.2025.11409694","is_oa":false,"landing_page_url":"https://doi.org/10.1109/sds68531.2025.11409694","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 12th International Conference on Software Defined Systems (SDS)","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/A5127866922","display_name":"Tomiwa Kunle Oluwadare","orcid":null},"institutions":[{"id":"https://openalex.org/I198012923","display_name":"University of Staffordshire","ror":"https://ror.org/00d6k8y35","country_code":"GB","type":"education","lineage":["https://openalex.org/I198012923"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Tomiwa Kunle Oluwadare","raw_affiliation_strings":["University of Staffordshire,School of Computing and Digital Technology,UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Staffordshire,School of Computing and Digital Technology,UK","institution_ids":["https://openalex.org/I198012923"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5061099244","display_name":"Pantaleon Lutta","orcid":"https://orcid.org/0000-0002-6358-8388"},"institutions":[{"id":"https://openalex.org/I198012923","display_name":"University of Staffordshire","ror":"https://ror.org/00d6k8y35","country_code":"GB","type":"education","lineage":["https://openalex.org/I198012923"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Pantaleon Lutta","raw_affiliation_strings":["University of Staffordshire,School of Computing and Digital Technology,UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Staffordshire,School of Computing and Digital Technology,UK","institution_ids":["https://openalex.org/I198012923"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I198012923"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.69229308,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"106","last_page":"113"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11644","display_name":"Spam and Phishing Detection","score":0.9664999842643738,"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":0.9664999842643738,"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/T11147","display_name":"Misinformation and Its Impacts","score":0.006800000090152025,"subfield":{"id":"https://openalex.org/subfields/3312","display_name":"Sociology and Political Science"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T13324","display_name":"Scientific and Engineering Research Topics","score":0.002300000051036477,"subfield":{"id":"https://openalex.org/subfields/3506","display_name":"Periodontics"},"field":{"id":"https://openalex.org/fields/35","display_name":"Dentistry"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/phishing","display_name":"Phishing","score":0.9294000267982483},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.70169997215271},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.6708999872207642},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.6313999891281128},{"id":"https://openalex.org/keywords/password","display_name":"Password","score":0.52920001745224},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.5282999873161316},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4912000000476837},{"id":"https://openalex.org/keywords/gradient-boosting","display_name":"Gradient boosting","score":0.453900009393692}],"concepts":[{"id":"https://openalex.org/C83860907","wikidata":"https://www.wikidata.org/wiki/Q135005","display_name":"Phishing","level":3,"score":0.9294000267982483},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7644000053405762},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.7250999808311462},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.70169997215271},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.6708999872207642},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6615999937057495},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.6313999891281128},{"id":"https://openalex.org/C109297577","wikidata":"https://www.wikidata.org/wiki/Q161157","display_name":"Password","level":2,"score":0.52920001745224},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.5282999873161316},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4912000000476837},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.453900009393692},{"id":"https://openalex.org/C119898033","wikidata":"https://www.wikidata.org/wiki/Q3433888","display_name":"Ensemble forecasting","level":2,"score":0.40450000762939453},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.36079999804496765},{"id":"https://openalex.org/C110083411","wikidata":"https://www.wikidata.org/wiki/Q1744628","display_name":"Statistical classification","level":2,"score":0.3402000069618225},{"id":"https://openalex.org/C137822555","wikidata":"https://www.wikidata.org/wiki/Q2587068","display_name":"Information sensitivity","level":2,"score":0.33309999108314514},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.3197999894618988},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.3163999915122986},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2720000147819519},{"id":"https://openalex.org/C113238511","wikidata":"https://www.wikidata.org/wiki/Q1071612","display_name":"k-nearest neighbors algorithm","level":2,"score":0.2720000147819519},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.25780001282691956}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/sds68531.2025.11409694","is_oa":false,"landing_page_url":"https://doi.org/10.1109/sds68531.2025.11409694","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 12th International Conference on Software Defined Systems (SDS)","raw_type":"proceedings-article"},{"id":"pmh:oai:eprints.staffs.ac.uk:9628","is_oa":false,"landing_page_url":"https://eprints.staffs.ac.uk/9628/1/URL%20Detection%20Using%20Ensemble%20Machine%20Learning%20Techniques%20-%20Accepted.docx","pdf_url":null,"source":{"id":"https://openalex.org/S4306401113","display_name":"Staffordshire Online Repository (Staffordshire University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I198012923","host_organization_name":"University of Staffordshire","host_organization_lineage":["https://openalex.org/I198012923"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":null,"raw_type":"Book Chapter, Section or Conference Proceeding"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.4122127294540405}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":4,"referenced_works":["https://openalex.org/W4280569680","https://openalex.org/W4313824596","https://openalex.org/W4319996419","https://openalex.org/W4389035844"],"related_works":[],"abstract_inverted_index":{"Phishing":[0],"is":[1,88],"a":[2,70,112,118,224],"cyberattack":[3],"that":[4,216],"uses":[5],"deceptive":[6],"emails,":[7,50],"websites,":[8],"or":[9,20],"messages":[10],"to":[11,29,45,60,78,89],"trick":[12],"individuals":[13],"into":[14],"revealing":[15],"sensitive":[16],"information":[17],"like":[18],"passwords":[19],"financial":[21],"details.":[22],"It":[23,132],"often":[24],"involves":[25,110],"imitating":[26],"trusted":[27],"entities":[28],"exploit":[30],"user":[31],"trust.":[32],"Researchers":[33],"have":[34],"tackled":[35],"phishing":[36,94,115,130,151,232],"by":[37,96],"developing":[38],"machine":[39,102,135],"learning":[40,43,76,136],"and":[41,51,64,128,142,157,178,196],"deep":[42],"algorithms":[44,177],"analyse":[46],"patterns":[47],"in":[48,72,230],"URLs,":[49],"website":[52],"features.":[53],"Recent":[54],"advancements":[55],"include":[56],"using":[57,111],"ensemble":[58,75,101,167,218],"methods":[59],"enhance":[61,90],"detection":[62,92,152],"accuracy":[63,229],"efficiency.":[65],"However,":[66],"the":[67,91,98,107,169,173,188,192,217],"literature":[68],"reveals":[69],"gap":[71],"effectively":[73],"combining":[74,162],"techniques":[77],"overcome":[79],"individual":[80,176],"model":[81,219],"limitations.":[82],"The":[83,213],"goal":[84],"of":[85,93,100,124,150,175,190,194,207,227],"this":[86],"research":[87],"URLs":[95,116,127],"harnessing":[97],"strength":[99],"learning.":[103],"To":[104],"combat":[105],"this,":[106],"proposed":[108],"solution":[109],"dataset":[113,121],"with":[114],"from":[117],"reliable":[119],"UCI":[120],"repository,":[122],"consisting":[123],"134,850":[125],"legitimate":[126],"100,945":[129],"URLs.":[131,233],"employs":[133],"multiple":[134],"algorithms:":[137],"Random":[138],"Forest,":[139],"Multilayer":[140],"Perceptron,":[141],"Gradient":[143],"Boosting":[144],"Machine,":[145],"each":[146],"handling":[147],"different":[148],"aspects":[149],"such":[153,184],"as":[154,185],"URL-based,":[155],"HTML-based,":[156],"derived":[158],"features":[159,183,199,209],"respectively.":[160],"By":[161],"these":[163],"models":[164],"through":[165],"an":[166],"approach,":[168],"system":[170],"can":[171],"mitigate":[172],"weaknesses":[174],"improve":[179],"predictive":[180],"performance.":[181],"Key":[182],"URL":[186],"length,":[187],"presence":[189],"HTTPS,":[191],"number":[193],"subdomains,":[195],"45":[197],"other":[198],"were":[200],"extracted":[201],"for":[202],"analysis":[203],"making":[204],"it":[205],"total":[206],"47":[208],"after":[210],"data":[211],"pre-processing.":[212],"results":[214],"show":[215],"outperformed":[220],"traditional":[221],"methods,":[222],"achieving":[223],"high":[225],"level":[226],"99.6%":[228],"detecting":[231]},"counts_by_year":[],"updated_date":"2026-08-21T09:56:20.448147","created_date":"2026-03-03T00:00:00"}