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

• Item type: Journal(1)
• 公開日: 2023-09-15
• 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: eng
• キーワード / 主題Scheme: Other
• キーワード / 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
• 著者所属(英): Tokai University / National Institute of Information and Communications Technology (NICT)
• 著者所属(英): en
• 著者所属(英): National Institute of Information and Communications Technology (NICT)
• 著者所属(英): en
• 著者所属(英): National Institute of Information and Communications Technology (NICT) / University of Hyogo
• 著者所属(英): en
• 著者所属(英): National Institute of Information and Communications Technology (NICT)
• 著者所属(英): en
• 著者所属(英): National Institute of Information and Communications Technology (NICT)
• 著者所属(英): en
• 著者所属(英): Tokai University / National Institute of Information and Communications Technology (NICT)
• 著者名: Hayato, Kimura; Keita, Emura; Takanori, Isobe; Ryoma, Ito; Kazuto, Ogawa; Toshihiro, Ohigashi
• 著者名(英): Hayato, Kimura; Keita, Emura; Takanori, Isobe; Ryoma, Ito; Kazuto, Ogawa; Toshihiro, Ohigashi
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 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
• 論文抄録(英) / 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
• 書誌レコードID / 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
• ISSN / 収録物識別子: 1882-7764
• 公開者 / 言語: ja
• 公開者 / 出版者: 情報処理学会