{"created":"2025-01-19T01:17:11.958028+00:00","updated":"2025-01-19T15:46:17.572599+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00216679","sets":["1164:6389:10832:10833"]},"path":["10833"],"owner":"44499","recid":"216679","title":["表層情報を用いたマルウェア検知精度を維持する機械学習モデル更新手法の検討"],"pubdate":{"attribute_name":"公開日","attribute_value":"2022-02-28"},"_buckets":{"deposit":"f4eb8b4c-3f65-4ebd-99cd-df28faf26a96"},"_deposit":{"id":"216679","pid":{"type":"depid","value":"216679","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"表層情報を用いたマルウェア検知精度を維持する機械学習モデル更新手法の検討","author_link":["559724","559720","559721","559723","559722","559725"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"表層情報を用いたマルウェア検知精度を維持する機械学習モデル更新手法の検討"},{"subitem_title":"Feasibility study of Updating Machine Learning Models to Maintain Malware Detection Accuracy Using Surface 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史弥"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"松木, 隆宏"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"寺田, 真敏"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Fumiya, Kurihara","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Takahiro, Matsuki","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Masato, Terada","creatorNameLang":"en"}],"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":"機械学習モデルを利用したマルウェア検知手法では，コンセプトドリフトと呼ばれる，機械学習モデルをトレーニングした時と予測する時のデータのずれが発生した場合に精度が落ちてしまうことが知られている．精度維持のため，ずれの発生したデータにラベルを付与し，再学習するというアプローチもあるが，学習データの作成に多大なコストがかかってしまうという課題がある．本稿では，この課題を解決するために，Fuzzy Hash 値を用いた機械学習モデル更新手法を提案する．提案方式は，マルウェア検知で高い精度を示した表層情報を利用することで，コストを抑えた機械学習モデル更新を実現すると共に，FFRI Dataset を用いた検証を通してその有効性を示す．","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"7","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":"2022-02-28","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"36","bibliographicVolumeNumber":"2022-SPT-46"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":216679,"links":{}}