{"created":"2025-01-19T01:15:20.241186+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00214523","sets":["6164:6165:6462:10749"]},"path":["10749"],"owner":"44499","recid":"214523","title":["フィッシングを検出するためのデータセット作成における注意点"],"pubdate":{"attribute_name":"公開日","attribute_value":"2021-10-19"},"_buckets":{"deposit":"a742d815-eab6-40d3-9f4e-3ccfc1040eb5"},"_deposit":{"id":"214523","pid":{"type":"depid","value":"214523","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"フィッシングを検出するためのデータセット作成における注意点","author_link":["551217","551215","551216","551218"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"フィッシングを検出するためのデータセット作成における注意点"},{"subitem_title":"The Points to Note When Making Datasets for Phishing Websites Detection","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"フィッシング，機械学習，データセット","subitem_subject_scheme":"Other"}]},"item_type_id":"18","publish_date":"2021-10-19","item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_18_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"長崎県立大学"},{"subitem_text_value":"長崎県立大学"}]},"item_18_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"University of Nagasaki","subitem_text_language":"en"},{"subitem_text_value":"University of Nagasaki","subitem_text_language":"en"}]},"item_publisher":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/214523/files/IPSJCSS2021123.pdf","label":"IPSJCSS2021123.pdf"},"date":[{"dateType":"Available","dateValue":"2023-10-19"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJCSS2021123.pdf","filesize":[{"value":"1.2 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"30"},{"tax":["include_tax"],"price":"0","billingrole":"46"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"246da225-c042-4f6f-ad21-a8dca05c52c5","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2021 by the Information Processing Society of Japan"}]},"item_18_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"中本, 雄一"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"加藤, 雅彦"}],"nameIdentifiers":[{}]}]},"item_18_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Yuichi, Nakamoto","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Masahiko, Katoh","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_5794","resourcetype":"conference paper"}]},"item_18_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"フィッシングは，サイバー空間における重大な脅威である．フィッシングの被害を防止するための技術的な対策が進められており，機械学習を利用してフィッシングを見分ける識別器を作成する研究が行われている．多くの研究において，どのような特徴量やアルゴリズムを用いるかという点に焦点が当てられる一方で，データセットについては議論されている論文は少ない．識別器を正しく評価するためには，データセットの内容も重要である．そこで，データセットを作成する際の注意点について調査を行い，フィッシングのデータソースの問題点について明らかにした．","subitem_description_type":"Other"}]},"item_18_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"Phishing is one of the most risky threats in cyber space. Technical measures are being taken to prevent phishing damage, researches of phishing detection using machine learning are in progress. While many studies focus on what features and algorithms are used, there is not much discussion about datasets. The contents of the dataset are also important for the correct evaluation of the classifier. Therefore, we investigated the points to be noted when creating datasets, and clarified the problems of the data source of phishing.","subitem_description_type":"Other"}]},"item_18_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"921","bibliographic_titles":[{"bibliographic_title":"コンピュータセキュリティシンポジウム2021論文集"}],"bibliographicPageStart":"914","bibliographicIssueDates":{"bibliographicIssueDate":"2021-10-19","bibliographicIssueDateType":"Issued"}}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":214523,"updated":"2025-01-19T16:35:39.280784+00:00","links":{}}