{"created":"2025-01-19T01:39:46.955993+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00237255","sets":["1164:3925:11477:11663"]},"path":["11663"],"owner":"44499","recid":"237255","title":["ナノ人工物メトリクスにおける機械学習に基づくクローン検知の一手法"],"pubdate":{"attribute_name":"公開日","attribute_value":"2024-07-15"},"_buckets":{"deposit":"5ae4429c-1cd7-45af-bdd3-a28eeb7905d2"},"_deposit":{"id":"237255","pid":{"type":"depid","value":"237255","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"ナノ人工物メトリクスにおける機械学習に基づくクローン検知の一手法","author_link":["649249","649244","649245","649242","649248","649243","649246","649247"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"ナノ人工物メトリクスにおける機械学習に基づくクローン検知の一手法"},{"subitem_title":"A Machine Learning-Based Clone Detection Approach in Nano-Artifact Metrics","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"BioX ","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2024-07-15","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"横浜国立大学"},{"subitem_text_value":"横浜国立大学"},{"subitem_text_value":"横浜国立大学"},{"subitem_text_value":"産業技術総合研究所"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Yokohama National University","subitem_text_language":"en"},{"subitem_text_value":"Yokohama National University","subitem_text_language":"en"},{"subitem_text_value":"Yokohama National University","subitem_text_language":"en"},{"subitem_text_value":"National Institute of Advanced Industrial Science and Technology","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/237255/files/IPSJ-CSEC24106049.pdf","label":"IPSJ-CSEC24106049.pdf"},"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-CSEC24106049.pdf","filesize":[{"value":"1.7 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"0","billingrole":"30"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_login","version_id":"43e0f682-2873-4903-8c64-96c525fbb8bd","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2024 by the Institute of Electronics, Information and Communication Engineers This SIG report is only available to those in membership of the SIG."}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"吉田, 直樹"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"宮本, 岩麒"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"吉岡, 克成"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"松本, 勉"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Naoki, Yoshida","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Iwaki, Miyamoto","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Katsunari, Yoshioka","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Tsutomu, Matsumoto","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA11235941","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":"2188-8655","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"人工物メトリクスは，人工物の物理的特徴を利用して認証を行う技術であり，ある特定の人工物になりすますクローンの脅威に備える必要がある．攻撃者が精度よくクローンを作製する場合には，オリジナルの製造方法とは異なる方法で作られるためにやや異なる特徴を持つ場合があり，顔認証における生体検知のように事前にクローンかどうかを判断できる可能性がある．本研究では電子線レジスト・ピラーの倒壊現象を利用したナノ人工物メトリクスを一例に，機械学習によってクローンを検知する手法について検討する．","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"Artifact metrics is a technology that uses the physical characteristics of an artifact for authentication, and it is necessary to prepare for the threat of clones that impersonate a particular artifact. When an attacker creates a clone with high accuracy, it may have slightly different characteristics because it is created by a different method than the original manufacturing method, and it may be possible to determine in advance whether it is a clone or not, as in the case of biometric detection in face recognition. In this study, we investigate a method to detect clones by machine learning, using nanoartifact metrics based on the resist collapse phenomenon in electron-beam lithography as an example.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"6","bibliographic_titles":[{"bibliographic_title":"研究報告コンピュータセキュリティ（CSEC）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2024-07-15","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"49","bibliographicVolumeNumber":"2024-CSEC-106"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":237255,"updated":"2025-01-19T08:56:33.049358+00:00","links":{}}