{"created":"2025-01-19T01:38:41.625294+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00236570","sets":["6504:11678:11687"]},"path":["11687"],"owner":"44499","recid":"236570","title":["機械学習を用いた攻撃検知のためのオーバーサンプリング手法の一検討"],"pubdate":{"attribute_name":"公開日","attribute_value":"2024-03-01"},"_buckets":{"deposit":"e2018803-4abb-4f31-bbc3-7ff93fb3fb23"},"_deposit":{"id":"236570","pid":{"type":"depid","value":"236570","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"機械学習を用いた攻撃検知のためのオーバーサンプリング手法の一検討","author_link":["646753","646755","646756","646754","646757"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"機械学習を用いた攻撃検知のためのオーバーサンプリング手法の一検討"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"セキュリティ","subitem_subject_scheme":"Other"}]},"item_type_id":"22","publish_date":"2024-03-01","item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_22_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"東北大"},{"subitem_text_value":"東北大"},{"subitem_text_value":"仙台高専"},{"subitem_text_value":"東北大"},{"subitem_text_value":"東北大"}]},"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/236570/files/IPSJ-Z86-6ZC-02.pdf","label":"IPSJ-Z86-6ZC-02.pdf"},"date":[{"dateType":"Available","dateValue":"2024-07-04"}],"format":"application/pdf","filename":"IPSJ-Z86-6ZC-02.pdf","filesize":[{"value":"502.5 kB"}],"mimetype":"application/pdf","accessrole":"open_date","version_id":"4a008fc7-c0bb-4a9e-a6fb-82df5ffbfb40","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2024 by the Information Processing Society of Japan"}]},"item_22_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"松井, 遼太朗"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"ルイス, ギリエ"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"和泉, 諭"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"水木, 敬明"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"菅沼, 拓夫"}],"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_22_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN00349328","subitem_source_identifier_type":"NCID"}]},"item_22_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"深刻化するサイバーセキュリティの脅威に対し、機械学習を用いて攻撃を検知する手法が数多く研究されている。これらの研究では、学習の際に用いるデータセットの多くはデータが不均衡であり、推定精度が低下する課題がある。この課題に対し、データセット内の少数派のデータ数を増やすことで不均衡問題を解決する、オーバーサンプリング手法がある。しかし、既存の手法では、意図しない不必要なデータが生成され、学習精度が下がってしまうため、その利用は限定的である。本研究では、多数派と少数派クラスの境界部のデータを集中的に増やす新たなオーバーサンプリング手法を提案する。本発表では、アルゴリズムの概要と基本設計を述べる。","subitem_description_type":"Other"}]},"item_22_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"520","bibliographic_titles":[{"bibliographic_title":"第86回全国大会講演論文集"}],"bibliographicPageStart":"519","bibliographicIssueDates":{"bibliographicIssueDate":"2024-03-01","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2024"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":236570,"updated":"2025-01-19T09:12:28.938127+00:00","links":{}}