{"id":227029,"updated":"2025-01-19T12:18:03.488049+00:00","links":{},"created":"2025-01-19T01:26:20.729717+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00227029","sets":["1164:6389:11170:11294"]},"path":["11294"],"owner":"44499","recid":"227029","title":["計測過程におけるノイズや伝達特性の影響を考慮した深層学習を用いたサイドチャネル解析手法の検討"],"pubdate":{"attribute_name":"公開日","attribute_value":"2023-07-17"},"_buckets":{"deposit":"2a2a19a0-702b-4859-b1c3-773e27a683d9"},"_deposit":{"id":"227029","pid":{"type":"depid","value":"227029","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"計測過程におけるノイズや伝達特性の影響を考慮した深層学習を用いたサイドチャネル解析手法の検討","author_link":["604038","604040","604037","604036","604041","604039"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"計測過程におけるノイズや伝達特性の影響を考慮した深層学習を用いたサイドチャネル解析手法の検討"},{"subitem_title":"Deep Learning-Based Side-Channel Analysis Considering the Effect of Noise and Transfer Characteristics on the Measurement Process","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"HWS","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2023-07-17","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"奈良先端科学技術大学院大学"},{"subitem_text_value":"奈良先端科学技術大学院大学"},{"subitem_text_value":"奈良先端科学技術大学院大学"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Nara Institute of Science and Technology","subitem_text_language":"en"},{"subitem_text_value":"Nara Institute of Science and Technology","subitem_text_language":"en"},{"subitem_text_value":"Nara Institute of 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/227029/files/IPSJ-SPT23052022.pdf","label":"IPSJ-SPT23052022.pdf"},"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-SPT23052022.pdf","filesize":[{"value":"1.9 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"0","billingrole":"46"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_login","version_id":"3d967003-6466-49d9-9a9b-7696e0d782b1","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2023 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":"北村, 圭輝"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"北澤, 太基"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"林, 優一"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Yoshiki, Kitamura","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Taiki, Kitazawa","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Yuichi, Hayashi","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":"深層学習を用いたサイドチャネル解析では，プロファイリングフェーズと攻撃フェーズでターゲットとなるデバイスやサイドチャネル情報を計測する環境が変化することにより学習モデルの移植性が低下し，これに起因して秘密鍵の推定精度も低下する．従来の検討では，移植性の低下を抑制するための学習モデルの生成方法に主眼が置かれていたが，攻撃フェーズにおいてどのような場所でサイドチャネル情報が計測されるかを事前に予測し，プロファイリングフェーズにおいて，最適な学習モデルを生成することは困難である．これに対し，本稿では，攻撃フェーズで計測したサイドチャネル情報をプロファイリングフェーズで計測したサイドチャネル情報の概形的特徴に近づけることにより，学習モデルの移植性低下を抑制する手法を提案し，AES の秘密鍵の推定するために必要となる波形数を指標として，提案手法を適用する場合としない場合を比較した．その結果，提案手法を用いない場合は，30,000 波形を用いても秘密鍵すべてを推定することが困難であったのに対し，提案手法を適用した場合は 13,000 波形を用いることで秘密鍵を推定可能であることが確認され，提案手法の有効性が示された．","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"5","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":"2023-07-17","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"22","bibliographicVolumeNumber":"2023-SPT-52"}]},"relation_version_is_last":true,"weko_creator_id":"44499"}}