{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,5]],"date-time":"2026-04-05T20:35:45Z","timestamp":1775421345193,"version":"3.50.1"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030778699","type":"print"},{"value":"9783030778705","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-77870-5_28","type":"book-chapter","created":{"date-parts":[[2021,6,15]],"date-time":"2021-06-15T23:11:50Z","timestamp":1623798710000},"page":"805-835","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":91,"title":["A Deeper Look at Machine Learning-Based Cryptanalysis"],"prefix":"10.1007","author":[{"given":"Adrien","family":"Benamira","sequence":"first","affiliation":[]},{"given":"David","family":"Gerault","sequence":"additional","affiliation":[]},{"given":"Thomas","family":"Peyrin","sequence":"additional","affiliation":[]},{"given":"Quan Quan","family":"Tan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,6,16]]},"reference":[{"key":"28_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"525","DOI":"10.1007\/978-3-662-46706-0_27","volume-title":"Fast Software Encryption","author":"F Abed","year":"2015","unstructured":"Abed, F., List, E., Lucks, S., Wenzel, J.: Differential cryptanalysis of round-reduced simon and speck. In: Cid, C., Rechberger, C. (eds.) FSE 2014. LNCS, vol. 8540, pp. 525\u2013545. Springer, Heidelberg (2015). https:\/\/doi.org\/10.1007\/978-3-662-46706-0_27"},{"key":"28_CR2","unstructured":"Beaulieu, R., Shors, D., Smith, J., Treatman-Clark, S., Weeks, B., Wingers, L.: The SIMON and SPECK families of lightweight block ciphers. IACR Cryptol. ePrint Arch. 2013, 404 (2013). http:\/\/eprint.iacr.org\/2013\/404"},{"key":"28_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"546","DOI":"10.1007\/978-3-662-46706-0_28","volume-title":"Fast Software Encryption","author":"A Biryukov","year":"2015","unstructured":"Biryukov, A., Roy, A., Velichkov, V.: Differential analysis of block ciphers SIMON and SPECK. In: Cid, C., Rechberger, C. (eds.) FSE 2014. LNCS, vol. 8540, pp. 546\u2013570. Springer, Heidelberg (2015). https:\/\/doi.org\/10.1007\/978-3-662-46706-0_28"},{"issue":"1","key":"28_CR4","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L.: Random forests. Mach. Learn. 45(1), 5\u201332 (2001)","journal-title":"Mach. Learn."},{"issue":"5","key":"28_CR5","doi-asserted-by":"publisher","first-page":"1726","DOI":"10.1016\/j.cor.2008.04.004","volume":"36","author":"J Castro","year":"2009","unstructured":"Castro, J., G\u00f3mez, D., Tejada, J.: Polynomial calculation of the shapley value based on sampling. Comput. Oper. Res. 36(5), 1726\u20131730 (2009)","journal-title":"Comput. Oper. Res."},{"key":"28_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1007\/978-3-319-13051-4_9","volume-title":"Selected Areas in Cryptography \u2013 SAC 2014","author":"I Dinur","year":"2014","unstructured":"Dinur, I.: Improved differential cryptanalysis of round-reduced speck. In: Joux, A., Youssef, A. (eds.) SAC 2014. LNCS, vol. 8781, pp. 147\u2013164. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-13051-4_9"},{"key":"28_CR7","doi-asserted-by":"crossref","unstructured":"Duan, X., Yue, C., Liu, H., Guo, H., Zhang, F.: Attitude tracking control of small-scale unmanned helicopters using quaternion-based adaptive dynamic surface control. IEEE Access 9, 10153\u201310165 (2021). https:\/\/doi.org\/10.1109\/ACCESS.2020.3043363","DOI":"10.1109\/ACCESS.2020.3043363"},{"key":"28_CR8","doi-asserted-by":"crossref","unstructured":"Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 1189\u20131232 (2001)","DOI":"10.1214\/aos\/1013203451"},{"key":"28_CR9","doi-asserted-by":"crossref","unstructured":"Fu, K.,Wang, M., Guo, Y., Sun, S., Hu, L.: MILP-based automatic search algorithms for differential and linear trails for speck. IACR Cryptol. ePrint Arch. 407 (2016)","DOI":"10.1007\/978-3-662-52993-5_14"},{"key":"28_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"584","DOI":"10.1007\/978-3-319-44953-1_37","volume-title":"Principles and Practice of Constraint Programming","author":"D Gerault","year":"2016","unstructured":"Gerault, D., Minier, M., Solnon, C.: Constraint programming models for chosen key differential cryptanalysis. In: Rueher, M. (ed.) CP 2016. LNCS, vol. 9892, pp. 584\u2013601. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-44953-1_37"},{"key":"28_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"150","DOI":"10.1007\/978-3-030-26951-7_6","volume-title":"Advances in Cryptology \u2013 CRYPTO 2019","author":"A Gohr","year":"2019","unstructured":"Gohr, A.: Improving attacks on round-reduced speck32\/64 using deep learning. In: Boldyreva, A., Micciancio, D. (eds.) CRYPTO 2019, Part II. LNCS, vol. 11693, pp. 150\u2013179. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-26951-7_6"},{"key":"28_CR12","unstructured":"Ke, G., et al.: Lightgbm: A highly efficient gradient boosting decision tree. Adv. Neural Inf. Process. Syst. 3146\u20133154 (2017)"},{"key":"28_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1007\/3-540-48658-5_3","volume-title":"Advances in Cryptology \u2014 CRYPTO \u201994","author":"SK Langford","year":"1994","unstructured":"Langford, S.K., Hellman, M.E.: Differential-linear cryptanalysis. In: Desmedt, Y.G. (ed.) CRYPTO 1994. LNCS, vol. 839, pp. 17\u201325. Springer, Heidelberg (1994). https:\/\/doi.org\/10.1007\/3-540-48658-5_3"},{"key":"28_CR14","unstructured":"Lundberg, S.M., et al.: Explainable AI for trees: From local explanations to global understanding. arXiv preprint arXiv:1905.04610 (2019)"},{"key":"28_CR15","unstructured":"Lundberg, S.M., Lee, S.I.: A unified approach to interpreting model predictions. In: Advances in Neural Information Processing systems, pp. 4765\u20134774 (2017)"},{"key":"28_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-319-49445-6_1","volume-title":"Security, Privacy, and Applied Cryptography Engineering","author":"H Maghrebi","year":"2016","unstructured":"Maghrebi, H., Portigliatti, T., Prouff, E.: Breaking cryptographic implementations using deep learning techniques. In: Carlet, C., Hasan, M.A., Saraswat, V. (eds.) SPACE 2016. LNCS, vol. 10076, pp. 3\u201326. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-49445-6_1"},{"key":"28_CR17","unstructured":"Mouha, N., Preneel, B.: A proof that the ARX cipher salsa20 is secure against differential cryptanalysis. IACR Cryptol. ePrint Arch. 328 (2013). http:\/\/eprint.iacr.org\/2013\/328"},{"key":"28_CR18","first-page":"57","volume":"2011","author":"N Mouha","year":"2011","unstructured":"Mouha, N., Wang, Q., Gu, D., Preneel, B.: Differential and linear cryptanalysis using mixed-integer linear programming. Inf. Secur. Cryptology - Inscrypt 2011, 57\u201376 (2011)","journal-title":"Inf. Secur. Cryptology - Inscrypt"},{"key":"28_CR19","unstructured":"Paszke, A., et al.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems, pp. 8026\u20138037 (2019)"},{"key":"28_CR20","unstructured":"Pedregosa, F., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011)"},{"key":"28_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"427","DOI":"10.1007\/3-540-57332-1_36","volume-title":"Advances in Cryptology \u2014 ASIACRYPT 1991","author":"RL Rivest","year":"1993","unstructured":"Rivest, R.L.: Cryptography and machine learning. In: Imai, H., Rivest, R.L., Matsumoto, T. (eds.) ASIACRYPT 1991. LNCS, vol. 739, pp. 427\u2013439. Springer, Heidelberg (1993). https:\/\/doi.org\/10.1007\/3-540-57332-1_36"},{"key":"28_CR22","unstructured":"Shrikumar, A., Greenside, P., Kundaje, A.: Learning important features through propagating activation differences. arXiv preprint arXiv:1704.02685 (2017)"},{"key":"28_CR23","unstructured":"Simonyan, K., Vedaldi, A., Zisserman, A.: Deep inside convolutional networks: Visualising image classification models and saliency maps. arXiv preprint arXiv:1312.6034 (2013)"},{"key":"28_CR24","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1007\/978-3-319-40367-0_24","volume-title":"Information Security and Privacy","author":"L Song","year":"2016","unstructured":"Song, L., Huang, Z., Yang, Q.: Automatic differential analysis of ARX block ciphers with application to SPECK and LEA. In: Liu, J.K., Steinfeld, R. (eds.) ACISP 2016. LNCS, vol. 9723, pp. 379\u2013394. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-40367-0_24"},{"issue":"1","key":"28_CR25","doi-asserted-by":"publisher","first-page":"281","DOI":"10.46586\/tosc.v2017.i1.281-306","volume":"2017","author":"S Sun","year":"2017","unstructured":"Sun, S., G\u00e9rault, D., Lafourcade, P., Yang, Q., Todo, Y., Qiao, K., Hu, L.: Analysis of AES, skinny, and others with constraint programming. IACR Trans. Symmetric Cryptol. 2017(1), 281\u2013306 (2017)","journal-title":"IACR Trans. Symmetric Cryptol."},{"key":"28_CR26","unstructured":"Sundararajan, M., Taly, A., Yan, Q.: Axiomatic attribution for deep networks. arXiv preprint arXiv:1703.01365 (2017)"},{"key":"28_CR27","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"818","DOI":"10.1007\/978-3-319-10590-1_53","volume-title":"Computer Vision \u2013 ECCV 2014","author":"MD Zeiler","year":"2014","unstructured":"Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part I. LNCS, vol. 8689, pp. 818\u2013833. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10590-1_53"},{"key":"28_CR28","unstructured":"Zhou, S., Wu, Y., Ni, Z., Zhou, X., Wen, H., Zou, Y.: Dorefa-net: Training low bitwidth convolutional neural networks with low bitwidth gradients. CoRR abs\/1606.06160 (2016). http:\/\/arxiv.org\/abs\/1606.06160"}],"container-title":["Lecture Notes in Computer Science","Advances in Cryptology \u2013 EUROCRYPT 2021"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-77870-5_28","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,16]],"date-time":"2024-06-16T00:14:10Z","timestamp":1718496850000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-77870-5_28"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030778699","9783030778705"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-77870-5_28","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"16 June 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"EUROCRYPT","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Annual International Conference on the Theory and Applications of Cryptographic Techniques","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Zagreb","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Croatia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 October 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 October 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"40","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eurocrypt2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eurocrypt.iacr.org\/2021\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"HotCRP","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"400","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"78","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"20% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"at least 3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"21","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}