Exploring Diverse Configurations of Cellular Automata Based S-Boxes Using Reinforcement Learning
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Springer Nature Switzerland
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https://link.springer.com/content/pdf/10.1007/978-3-031-71552-5_10
Reference9 articles.
1. Ayyagari, T., Saji, A., John, A., Jose, J.: Exploring lightweight s-boxes using cellular automata and reinforcement learning. In: Chopard, B., Bandini, S., Dennunzio, A., Arabi Haddad, M. (eds.) ACRI 2022. LNCS, vol. 13402, pp. 17–28. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-14926-9_2
2. Mariot, L., Manzoni, L.: On the linear components space of s-boxes generated by orthogonal cellular automata. In: Chopard, B., Bandini, S., Dennunzio, A., Arabi Haddad, M. (eds.) ACRI 2022. LNCS, vol. 13402, pp. 52–62. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-14926-9_5
3. Mariot, L., Manzoni, L.: A Classification of S-boxes Generated by Orthogonal Cellular Automata, vol. 23, pp. 5–16. Kluwer Academic Publishers, USA (2023). https://doi.org/10.1007/s11047-023-09956-z
4. Mariot, L., Saletta, M., Leporati, A., Manzoni, L.: Exploring semi-bent boolean functions arising from cellular automata. In: Gwizdałła, T.M., Manzoni, L., Sirakoulis, G.C., Bandini, S., Podlaski, K. (eds.) ACRI 2020. LNCS, vol. 12599, pp. 56–66. Springer, Heidelberg (2020). https://doi.org/10.1007/978-3-030-69480-7_7
5. Lecture Notes in Computer Science;W Meier,1991
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