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  1. 論文誌(ジャーナル)
  2. Vol.64
  3. No.9

A Deeper Look into Deep Learning-based Output Prediction Attacks Using Weak SPN Block Ciphers

https://ipsj.ixsq.nii.ac.jp/records/227702
https://ipsj.ixsq.nii.ac.jp/records/227702
872bdfdc-737c-4cda-bdc8-1c65b5ef7b7f
名前 / ファイル ライセンス アクション
IPSJ-JNL6409007.pdf IPSJ-JNL6409007.pdf (1.2 MB)
 2025年9月15日からダウンロード可能です。
Copyright (c) 2023 by the Information Processing Society of Japan
非会員:¥0, IPSJ:学会員:¥0, 論文誌:会員:¥0, DLIB:会員:¥0
Item type Journal(1)
公開日 2023-09-15
タイトル
タイトル A Deeper Look into Deep Learning-based Output Prediction Attacks Using Weak SPN Block Ciphers
タイトル
言語 en
タイトル A Deeper Look into Deep Learning-based Output Prediction Attacks Using Weak SPN Block Ciphers
言語
言語 eng
キーワード
主題Scheme Other
主題 [特集:サイバー空間を安全にするコンピュータセキュリティ技術] deep learning, block cipher, SPN
資源タイプ
資源タイプ識別子 http://purl.org/coar/resource_type/c_6501
資源タイプ journal article
著者所属
Tokai University/National Institute of Information and Communications Technology (NICT)
著者所属
National Institute of Information and Communications Technology (NICT)
著者所属
National Institute of Information and Communications Technology (NICT)/University of Hyogo
著者所属
National Institute of Information and Communications Technology (NICT)
著者所属
National Institute of Information and Communications Technology (NICT)
著者所属
Tokai University/National Institute of Information and Communications Technology (NICT)
著者所属(英)
en
Tokai University / National Institute of Information and Communications Technology (NICT)
著者所属(英)
en
National Institute of Information and Communications Technology (NICT)
著者所属(英)
en
National Institute of Information and Communications Technology (NICT) / University of Hyogo
著者所属(英)
en
National Institute of Information and Communications Technology (NICT)
著者所属(英)
en
National Institute of Information and Communications Technology (NICT)
著者所属(英)
en
Tokai University / National Institute of Information and Communications Technology (NICT)
著者名 Hayato, Kimura

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Hayato, Kimura

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Keita, Emura

× Keita, Emura

Keita, Emura

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Takanori, Isobe

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Takanori, Isobe

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Ryoma, Ito

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Ryoma, Ito

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Kazuto, Ogawa

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Kazuto, Ogawa

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Toshihiro, Ohigashi

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Toshihiro, Ohigashi

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著者名(英) Hayato, Kimura

× Hayato, Kimura

en Hayato, Kimura

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Keita, Emura

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en Keita, Emura

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Takanori, Isobe

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en Takanori, Isobe

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Ryoma, Ito

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en Ryoma, Ito

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Kazuto, Ogawa

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en Kazuto, Ogawa

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Toshihiro, Ohigashi

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論文抄録
内容記述タイプ Other
内容記述 Cryptanalysis in a blackbox setting using deep learning is powerful because it does not require the attacker to have knowledge about the internal structure of the cryptographic algorithm. Thus, it is necessary to design a symmetric key cipher that is secure against cryptanalysis using deep learning. Kimura et al. (AIoTS 2022) investigated deep learning-based attacks on the small PRESENT-[4] block cipher with limited component changes, identifying characteristics specific to these attacks which remain unaffected by linear/differential cryptanalysis. Finding such characteristics is important because exploiting such characteristics can make the target cipher vulnerable to deep learning-based attacks. Thus, this paper extends a previous method to explore clues for designing symmetric-key cryptographic algorithms that are secure against deep learning-based attacks. We employ small PRESENT-[4] with two weak S-boxes, which are known to be weak against differential/linear attacks, to clarify the relationship between classical and deep learning-based attacks. As a result, we demonstrated the success probability of our deep learning-based whitebox analysis tends to be affected by the success probability of classical cryptanalysis methods. And we showed our whitebox analysis achieved the same attack capability as traditional methods even when the S-box of the target cipher was changed to a weak one.
------------------------------
This is a preprint of an article intended for publication Journal of
Information Processing(JIP). This preprint should not be cited. This
article should be cited as: Journal of Information Processing Vol.31(2023) (online)
DOI http://dx.doi.org/10.2197/ipsjjip.31.550
------------------------------
論文抄録(英)
内容記述タイプ Other
内容記述 Cryptanalysis in a blackbox setting using deep learning is powerful because it does not require the attacker to have knowledge about the internal structure of the cryptographic algorithm. Thus, it is necessary to design a symmetric key cipher that is secure against cryptanalysis using deep learning. Kimura et al. (AIoTS 2022) investigated deep learning-based attacks on the small PRESENT-[4] block cipher with limited component changes, identifying characteristics specific to these attacks which remain unaffected by linear/differential cryptanalysis. Finding such characteristics is important because exploiting such characteristics can make the target cipher vulnerable to deep learning-based attacks. Thus, this paper extends a previous method to explore clues for designing symmetric-key cryptographic algorithms that are secure against deep learning-based attacks. We employ small PRESENT-[4] with two weak S-boxes, which are known to be weak against differential/linear attacks, to clarify the relationship between classical and deep learning-based attacks. As a result, we demonstrated the success probability of our deep learning-based whitebox analysis tends to be affected by the success probability of classical cryptanalysis methods. And we showed our whitebox analysis achieved the same attack capability as traditional methods even when the S-box of the target cipher was changed to a weak one.
------------------------------
This is a preprint of an article intended for publication Journal of
Information Processing(JIP). This preprint should not be cited. This
article should be cited as: Journal of Information Processing Vol.31(2023) (online)
DOI http://dx.doi.org/10.2197/ipsjjip.31.550
------------------------------
書誌レコードID
収録物識別子タイプ NCID
収録物識別子 AN00116647
書誌情報 情報処理学会論文誌

巻 64, 号 9, 発行日 2023-09-15
ISSN
収録物識別子タイプ ISSN
収録物識別子 1882-7764
公開者
言語 ja
出版者 情報処理学会
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