{"created":"2025-01-19T01:13:07.875708+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00212113","sets":["1164:6389:10492:10642"]},"path":["10642"],"owner":"44499","recid":"212113","title":["マルウェア検知に対するバックドアポイズニング攻撃の対策としてのオートエンコーダの定量的評価"],"pubdate":{"attribute_name":"公開日","attribute_value":"2021-07-12"},"_buckets":{"deposit":"5ec4df26-94eb-4c08-8111-01b812838719"},"_deposit":{"id":"212113","pid":{"type":"depid","value":"212113","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"マルウェア検知に対するバックドアポイズニング攻撃の対策としてのオートエンコーダの定量的評価","author_link":["540345","540348","540346","540349","540347","540344"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"マルウェア検知に対するバックドアポイズニング攻撃の対策としてのオートエンコーダの定量的評価"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"CSEC","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2021-07-12","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"東北大学"},{"subitem_text_value":"KDDI総合研究所"},{"subitem_text_value":"KDDI総合研究所"},{"subitem_text_value":"九州大学"},{"subitem_text_value":"東北大学"},{"subitem_text_value":"東北大学"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Tohoku University","subitem_text_language":"en"},{"subitem_text_value":"KDDI Research, Inc.","subitem_text_language":"en"},{"subitem_text_value":"KDDI Research, Inc.","subitem_text_language":"en"},{"subitem_text_value":"Kyushu University","subitem_text_language":"en"},{"subitem_text_value":"Tohoku University","subitem_text_language":"en"},{"subitem_text_value":"Tohoku University","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"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/212113/files/IPSJ-SPT21043001.pdf","label":"IPSJ-SPT21043001.pdf"},"date":[{"dateType":"Available","dateValue":"2023-07-12"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-SPT21043001.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":"46"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"d15c829b-7d98-49a3-b915-22eee729ac9a","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2021 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"松本, 悠希"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"成定, 真太郎"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"披田野, 清良"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"内林, 俊洋"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"菅沼, 拓夫"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"樋地, 正浩"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA12628305","subitem_source_identifier_type":"NCID"}]},"item_4_textarea_12":{"attribute_name":"Notice","attribute_value_mlt":[{"subitem_textarea_value":"SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc."}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_18gh","resourcetype":"technical report"}]},"item_4_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2188-8671","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"マルウェア解析では，亜種の増加により解析者の負担が増大しており，効率的にマルウェアを検知可能な機械学習による静的解析の需要が高まっている．一方，機械学習に対する代表的な脅威として，バックドアポイズニング攻撃がある．本攻撃では，訓練データにポイズニングデータを混入することで，特定のデータを誤分類させる．Severi らの報告により，本攻撃は，機械学習を用いたマルウェア検知へも応用できることが明らかとなっている．このため，機械学習によりマルウェアを高精度に検知するためには，ポイズニングデータの影響を受けない攻撃耐性の高い検知モデルの構築が必要となる．本稿では，検知モデルを構築する際にポイズニングデータの含まれていないクリーンなデータを入手できない状況を想定し，ポイズニングデータ入りの訓練データに対してオートエンコーダを適用することでポイズニングデータの効果を排除する方法を提案する．そして，実データを用いた評価実験を通して，提案手法により，マルウェアの検知精度の低下を最小限に抑えつつ，バックドア攻撃の影響を大幅に低減できることを示す．","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"8","bibliographic_titles":[{"bibliographic_title":"研究報告セキュリティ心理学とトラスト（SPT）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2021-07-12","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2021-SPT-43"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":212113,"updated":"2025-01-19T17:36:00.283253+00:00","links":{}}