{"id":"https://openalex.org/W3135094089","doi":"https://doi.org/10.1109/iceic51217.2021.9369758","title":"Learning Disentangled Representation of Web Address via Convolutional-Recurrent Triplet Network for Classifying Phishing URLs","display_name":"Learning Disentangled Representation of Web Address via Convolutional-Recurrent Triplet Network for Classifying Phishing URLs","publication_year":2021,"publication_date":"2021-01-31","ids":{"openalex":"https://openalex.org/W3135094089","doi":"https://doi.org/10.1109/iceic51217.2021.9369758","mag":"3135094089"},"language":"en","primary_location":{"id":"doi:10.1109/iceic51217.2021.9369758","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iceic51217.2021.9369758","pdf_url":null,"source":{"id":"https://openalex.org/S4306498844","display_name":"2021 International Conference on Electronics, Information, and Communication (ICEIC)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Conference on Electronics, Information, and Communication (ICEIC)","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/A5112483522","display_name":"Seok-Jun Bu","orcid":null},"institutions":[{"id":"https://openalex.org/I193775966","display_name":"Yonsei University","ror":"https://ror.org/01wjejq96","country_code":"KR","type":"education","lineage":["https://openalex.org/I193775966"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Seok-Jun Bu","raw_affiliation_strings":["dept. of Computer Science, Yonsei University, Seoul, Korea"],"affiliations":[{"raw_affiliation_string":"dept. of Computer Science, Yonsei University, Seoul, Korea","institution_ids":["https://openalex.org/I193775966"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5058835899","display_name":"Hae-Jung Kim","orcid":null},"institutions":[{"id":"https://openalex.org/I4210113474","display_name":"Kyungil University","ror":"https://ror.org/024kwvm84","country_code":"KR","type":"education","lineage":["https://openalex.org/I4210113474"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Hae-Jung Kim","raw_affiliation_strings":["dept. of Computer Engineering, Kyungil University, Daegu, Korea"],"affiliations":[{"raw_affiliation_string":"dept. of Computer Engineering, Kyungil University, Daegu, Korea","institution_ids":["https://openalex.org/I4210113474"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5112483522"],"corresponding_institution_ids":["https://openalex.org/I193775966"],"apc_list":null,"apc_paid":null,"fwci":0.2836,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.41203093,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":93},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"4"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11644","display_name":"Spam and Phishing Detection","score":1.0,"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":1.0,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9871000051498413,"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/T11147","display_name":"Misinformation and Its Impacts","score":0.9855999946594238,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/phishing","display_name":"Phishing","score":0.8432095646858215},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8146296739578247},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6198374032974243},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5835248231887817},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5794912576675415},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.5008089542388916},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.47136417031288147},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4579862356185913},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4476177990436554},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.43298354744911194},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.32124102115631104},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.17835640907287598},{"id":"https://openalex.org/keywords/the-internet","display_name":"The Internet","score":0.17405229806900024}],"concepts":[{"id":"https://openalex.org/C83860907","wikidata":"https://www.wikidata.org/wiki/Q135005","display_name":"Phishing","level":3,"score":0.8432095646858215},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8146296739578247},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6198374032974243},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5835248231887817},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5794912576675415},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.5008089542388916},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.47136417031288147},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4579862356185913},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4476177990436554},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.43298354744911194},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32124102115631104},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.17835640907287598},{"id":"https://openalex.org/C110875604","wikidata":"https://www.wikidata.org/wiki/Q75","display_name":"The Internet","level":2,"score":0.17405229806900024},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iceic51217.2021.9369758","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iceic51217.2021.9369758","pdf_url":null,"source":{"id":"https://openalex.org/S4306498844","display_name":"2021 International Conference on Electronics, Information, and Communication (ICEIC)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Conference on Electronics, Information, and Communication (ICEIC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.5199999809265137}],"awards":[{"id":"https://openalex.org/G8087878142","display_name":null,"funder_award_id":"2018B-010","funder_id":"https://openalex.org/F4320311039","funder_display_name":"Ministry of Higher Education and Scientific Research"}],"funders":[{"id":"https://openalex.org/F4320311039","display_name":"Ministry of Higher Education and Scientific Research","ror":"https://ror.org/00kab6t91"},{"id":"https://openalex.org/F4320322120","display_name":"National Research Foundation of Korea","ror":"https://ror.org/013aysd81"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":7,"referenced_works":["https://openalex.org/W1985920648","https://openalex.org/W2146729596","https://openalex.org/W2625935159","https://openalex.org/W2806164097","https://openalex.org/W2913334908","https://openalex.org/W2913694443","https://openalex.org/W3015534721"],"related_works":["https://openalex.org/W2149202530","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3193565141","https://openalex.org/W3133861977","https://openalex.org/W3167935049","https://openalex.org/W3103566983","https://openalex.org/W3029198973","https://openalex.org/W3048601286","https://openalex.org/W2965925734"],"abstract_inverted_index":{"Deep":[0],"learning":[1,116,148],"models":[2],"for":[3,13],"phishing":[4,45,154],"URL":[5,44,65,73,98,155],"classification,":[6],"based":[7,117],"on":[8],"the":[9,15,34,39,43,59,64,70,87,113,143,146,151],"convolutional-recurrent":[10],"neural":[11],"network":[12,83,122],"modeling":[14],"character-level":[16],"and":[17,47,77,105,132],"word-level":[18],"features,":[19],"have":[20,30],"achieved":[21,107],"good":[22],"performance":[23,109],"in":[24,33,49,63,67,139,150],"terms":[25,68],"of":[26,42,69,145,153],"accuracy.":[27],"However,":[28],"there":[29],"been":[31],"issues":[32,62],"sampling":[35],"stage":[36],"due":[37],"to":[38,57,78,112,141],"class":[40,60],"imbalance":[41,61],"data":[46],"problems":[48],"constructing":[50],"feature":[51,74],"spaces.":[52],"Therefore,":[53],"this":[54],"study":[55],"aimed":[56],"address":[58],"domain,":[66],"deep":[71,115],"learning-based":[72],"space":[75],"generation,":[76],"propose":[79],"a":[80,108,126,135],"modified":[81,120],"triplet":[82,121],"structure":[84],"that":[85],"learns":[86],"similarity":[88],"between":[89],"URLs.":[90],"The":[91,119],"proposed":[92],"method":[93],"was":[94,123],"verified":[95],"using":[96],"60,000":[97],"datasets":[99],"collected":[100],"from":[101],"real-world":[102],"web":[103],"addresses,":[104],"it":[106,133],"improvement":[110,138],"compared":[111],"latest":[114],"methods.":[118],"evaluated":[124],"by":[125],"10-fold":[127],"cross-validation":[128],"per":[129],"time":[130],"resolution,":[131],"demonstrated":[134],"45":[136],"percent":[137],"recall":[140],"confirm":[142],"validity":[144],"metric":[147],"approach":[149],"field":[152],"classification.":[156]},"counts_by_year":[{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
