{"id":227702,"updated":"2025-01-19T12:03:50.018262+00:00","links":{},"created":"2025-01-19T01:26:57.411128+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00227702","sets":["581:11107:11118"]},"path":["11118"],"owner":"44499","recid":"227702","title":["A Deeper Look into Deep Learning-based Output Prediction Attacks Using Weak SPN Block Ciphers"],"pubdate":{"attribute_name":"公開日","attribute_value":"2023-09-15"},"_buckets":{"deposit":"d9bf0126-96fc-4e0a-893e-8ba50bf79f1f"},"_deposit":{"id":"227702","pid":{"type":"depid","value":"227702","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"A Deeper Look into Deep Learning-based Output Prediction Attacks Using Weak SPN Block Ciphers","author_link":["606833","606831","606826","606825","606824","606827","606830","606829","606822","606832","606823","606828"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"A Deeper Look into Deep Learning-based Output Prediction Attacks Using Weak SPN Block Ciphers"},{"subitem_title":"A Deeper Look into Deep Learning-based Output Prediction Attacks Using Weak SPN Block Ciphers","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"[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","subitem_subject_scheme":"Other"}]},"item_type_id":"2","publish_date":"2023-09-15","item_2_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"Tokai University／National Institute of Information and Communications Technology (NICT)"},{"subitem_text_value":"National Institute of Information and Communications Technology (NICT)"},{"subitem_text_value":"National Institute of Information and Communications Technology (NICT)／University of Hyogo"},{"subitem_text_value":"National Institute of Information and Communications Technology (NICT)"},{"subitem_text_value":"National Institute of Information and Communications Technology (NICT)"},{"subitem_text_value":"Tokai University／National Institute of Information and Communications Technology (NICT)"}]},"item_2_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Tokai University / National Institute of Information and Communications Technology (NICT)","subitem_text_language":"en"},{"subitem_text_value":"National Institute of Information and Communications Technology (NICT)","subitem_text_language":"en"},{"subitem_text_value":"National Institute of Information and Communications Technology (NICT) / University of Hyogo","subitem_text_language":"en"},{"subitem_text_value":"National Institute of Information and Communications Technology (NICT)","subitem_text_language":"en"},{"subitem_text_value":"National Institute of Information and Communications Technology (NICT)","subitem_text_language":"en"},{"subitem_text_value":"Tokai University / National Institute of Information and Communications Technology (NICT)","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"publish_status":"0","weko_shared_id":44499,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/227702/files/IPSJ-JNL6409007.pdf","label":"IPSJ-JNL6409007.pdf"},"date":[{"dateType":"Available","dateValue":"2025-09-15"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-JNL6409007.pdf","filesize":[{"value":"1.2 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"0","billingrole":"5"},{"tax":["include_tax"],"price":"0","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"8"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"8e08340d-a31e-4565-a88c-4d4cce7e7edf","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2023 by the Information Processing Society of Japan"}]},"item_2_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Hayato, Kimura"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Keita, Emura"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Takanori, Isobe"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Ryoma, Ito"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Kazuto, Ogawa"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Toshihiro, Ohigashi"}],"nameIdentifiers":[{}]}]},"item_2_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Hayato, Kimura","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Keita, Emura","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Takanori, Isobe","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Ryoma, Ito","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Kazuto, Ogawa","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Toshihiro, Ohigashi","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_2_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN00116647","subitem_source_identifier_type":"NCID"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_6501","resourcetype":"journal article"}]},"item_2_publisher_15":{"attribute_name":"公開者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"item_2_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"1882-7764","subitem_source_identifier_type":"ISSN"}]},"item_2_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"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.\n------------------------------\nThis is a preprint of an article intended for publication Journal of\nInformation Processing(JIP). This preprint should not be cited. This\narticle should be cited as: Journal of Information Processing Vol.31(2023) (online)\nDOI　http://dx.doi.org/10.2197/ipsjjip.31.550\n------------------------------","subitem_description_type":"Other"}]},"item_2_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"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.\n------------------------------\nThis is a preprint of an article intended for publication Journal of\nInformation Processing(JIP). This preprint should not be cited. This\narticle should be cited as: Journal of Information Processing Vol.31(2023) (online)\nDOI　http://dx.doi.org/10.2197/ipsjjip.31.550\n------------------------------","subitem_description_type":"Other"}]},"item_2_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographic_titles":[{"bibliographic_title":"情報処理学会論文誌"}],"bibliographicIssueDates":{"bibliographicIssueDate":"2023-09-15","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"9","bibliographicVolumeNumber":"64"}]},"relation_version_is_last":true,"weko_creator_id":"44499"}}