Improving deep learning-based neural distinguisher with multiple ciphertext pairs for Speck and Simon

Improving deep learning-based neural distinguisher with multiple ciphertext pairs for Speck and Simon | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Improving deep learning-based neural distinguisher with multiple ciphertext pairs for Speck and Simon Yufei Hou, Jie Liu, Shouxu Han, Zhongjun Ma, Xi Ye, Xuan Nie This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5964357/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 Apr, 2025 Read the published version in Scientific Reports → Version 1 posted 13 You are reading this latest preprint version Abstract The neural network-based differential distinguisher has attracted significant interest from researchers due to its high efficiency in cryptanalysis since its introduction by Gohr in 2019. However, the accuracy of existing neural distinguishers remains limited for high-round-reduced cryptosystems. In this work, we explore the design principles of neural networks and propose a novel neural distinguisher based on a multi-scale convolutional block and dense residual connections. Two different ablation schemes are designed to verify the efficiency of the proposed neural distinguisher. Additionally, the concept of a linear attack is introduced to optimize the input dataset for the neural distinguisher. By combining ciphertext pairs, the differences between ciphertext pairs, the keys, and the differences between the keys, a novel dataset model is designed. The results show that the accuracy of the proposed neural distinguisher, utilizing the novel neural network and dataset, is 0.15–0.45% higher than Gohr’s distinguisher for Speck 32/64 when using a single ciphertext pair as input. When using multiple ciphertext pairs as input, it is 1.24–3.5% higher than the best distinguishers for Speck 32/64 and 0.32–1.83% higher than the best distinguishers for Simon 32/64. Finally, a key recovery attack based on the proposed neural distinguisher using a single ciphertext pair is implemented, achieving a success rate of 61.8%, which is 9.7% higher than the distinguisher proposed by Gohr. Therefore, the proposed neural distinguisher demonstrates significant advantages in both accuracy and key recovery rate. Physical sciences/Mathematics and computing/Computer science Physical sciences/Mathematics and computing/Information technology Neural distinguisher differential analysis key recovery attack deep learning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 21 Apr, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 04 Apr, 2025 Editor assigned by journal 04 Apr, 2025 Reviewers agreed at journal 31 Mar, 2025 Reviews received at journal 29 Mar, 2025 Reviews received at journal 26 Mar, 2025 Reviewers agreed at journal 26 Mar, 2025 Reviewers agreed at journal 26 Mar, 2025 Reviewers agreed at journal 24 Mar, 2025 Reviews received at journal 24 Mar, 2025 Reviewers agreed at journal 24 Mar, 2025 Reviewers invited by journal 24 Mar, 2025 Submission checks completed at journal 19 Mar, 2025 First submitted to journal 18 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5964357","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":433373308,"identity":"74cda88a-6956-4eb3-b512-fafb14435f76","order_by":0,"name":"Yufei Hou","email":"","orcid":"","institution":"Northwestern Polytechnical University","correspondingAuthor":false,"prefix":"","firstName":"Yufei","middleName":"","lastName":"Hou","suffix":""},{"id":433373309,"identity":"9b99ec20-c2e1-41a0-a1c0-dd6b94fbe75a","order_by":1,"name":"Jie 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analysis, key recovery attack, deep learning","lastPublishedDoi":"10.21203/rs.3.rs-5964357/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5964357/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe neural network-based differential distinguisher has attracted significant interest from researchers due to its high efficiency in cryptanalysis since its introduction by Gohr in 2019. However, the accuracy of existing neural distinguishers remains limited for high-round-reduced cryptosystems. In this work, we explore the design principles of neural networks and propose a novel neural distinguisher based on a multi-scale convolutional block and dense residual connections. Two different ablation schemes are designed to verify the efficiency of the proposed neural distinguisher. Additionally, the concept of a linear attack is introduced to optimize the input dataset for the neural distinguisher. By combining ciphertext pairs, the differences between ciphertext pairs, the keys, and the differences between the keys, a novel dataset model is designed. The results show that the accuracy of the proposed neural distinguisher, utilizing the novel neural network and dataset, is 0.15\u0026ndash;0.45% higher than Gohr\u0026rsquo;s distinguisher for Speck 32/64 when using a single ciphertext pair as input. When using multiple ciphertext pairs as input, it is 1.24\u0026ndash;3.5% higher than the best distinguishers for Speck 32/64 and 0.32\u0026ndash;1.83% higher than the best distinguishers for Simon 32/64. Finally, a key recovery attack based on the proposed neural distinguisher using a single ciphertext pair is implemented, achieving a success rate of 61.8%, which is 9.7% higher than the distinguisher proposed by Gohr. Therefore, the proposed neural distinguisher demonstrates significant advantages in both accuracy and key recovery rate.\u003c/p\u003e","manuscriptTitle":"Improving deep learning-based neural distinguisher with multiple ciphertext pairs for Speck and Simon","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-25 17:35:21","doi":"10.21203/rs.3.rs-5964357/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-04-04T04:43:39+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-04T04:40:31+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"126199302956017631201751956204580543210","date":"2025-03-31T05:04:02+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-30T01:42:04+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-27T00:58:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"150474544836344539852251513442251059635","date":"2025-03-26T12:39:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"267494903658958506940554236028986027738","date":"2025-03-26T06:57:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"238397940721792555593218079966469446089","date":"2025-03-24T19:14:11+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-24T14:10:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"92864745095829306029759095438500499564","date":"2025-03-24T11:51:35+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-24T09:36:23+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-19T08:43:08+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-03-18T15:36:08+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0c3216e0-b264-4389-a40f-5fc73113103b","owner":[],"postedDate":"March 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":46146301,"name":"Physical sciences/Mathematics and computing/Computer science"},{"id":46146302,"name":"Physical sciences/Mathematics and computing/Information technology"}],"tags":[],"updatedAt":"2025-04-28T15:59:44+00:00","versionOfRecord":{"articleIdentity":"rs-5964357","link":"https://doi.org/10.1038/s41598-025-98251-1","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-04-21 15:57:11","publishedOnDateReadable":"April 21st, 2025"},"versionCreatedAt":"2025-03-25 17:35:21","video":"","vorDoi":"10.1038/s41598-025-98251-1","vorDoiUrl":"https://doi.org/10.1038/s41598-025-98251-1","workflowStages":[]},"version":"v1","identity":"rs-5964357","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5964357","identity":"rs-5964357","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}