Voir la notice de l'article provenant de la source Math-Net.Ru
@article{BGUMI_2024_2_a6, author = {P. P. Urbanovich and N. P. Shutko}, title = {Usage of hypercomplex numbers in a cryptographic key agreement protocol based on neural networks}, journal = {Journal of the Belarusian State University. Mathematics and Informatics}, pages = {81--92}, publisher = {mathdoc}, volume = {2}, year = {2024}, language = {ru}, url = {http://geodesic.mathdoc.fr/item/BGUMI_2024_2_a6/} }
TY - JOUR AU - P. P. Urbanovich AU - N. P. Shutko TI - Usage of hypercomplex numbers in a cryptographic key agreement protocol based on neural networks JO - Journal of the Belarusian State University. Mathematics and Informatics PY - 2024 SP - 81 EP - 92 VL - 2 PB - mathdoc UR - http://geodesic.mathdoc.fr/item/BGUMI_2024_2_a6/ LA - ru ID - BGUMI_2024_2_a6 ER -
%0 Journal Article %A P. P. Urbanovich %A N. P. Shutko %T Usage of hypercomplex numbers in a cryptographic key agreement protocol based on neural networks %J Journal of the Belarusian State University. Mathematics and Informatics %D 2024 %P 81-92 %V 2 %I mathdoc %U http://geodesic.mathdoc.fr/item/BGUMI_2024_2_a6/ %G ru %F BGUMI_2024_2_a6
P. P. Urbanovich; N. P. Shutko. Usage of hypercomplex numbers in a cryptographic key agreement protocol based on neural networks. Journal of the Belarusian State University. Mathematics and Informatics, Tome 2 (2024), pp. 81-92. http://geodesic.mathdoc.fr/item/BGUMI_2024_2_a6/
[1] W. Diffie, M. Hellman, “New directions in cryptography”, IEEE Transactions on Information Theory, 22(6) (1976), 644–654 | DOI
[2] W. Kinzel, I. Kanter, “Neural cryptography”, Proceedings of the 9th International conference on neural information processing (Singapore), Orchid Country Club, Singapore, 2002, 1351–1354 | DOI
[3] A. Klimov, A. Mityagin, A. Shamir, “Analysis of neural cryptography”, Advances in Cryptology – ASIACRYPT 2002, Springer, Berlin, 2002, 288–298 (Lecture notes in computer science; volume 2501) | DOI
[4] M. Rosen-Zvi, I. Kanter, W. Kinzel, “Cryptography based on neural networks analytical results”, Journal of Physics A. Mathematical and General, 35(47) (2002), 707–713 | DOI
[5] M. Plonkovski, P. P. Urbanovich, “Kriptograficheskoe preobrazovanie informatsii na osnove neirosetevykh tekhnologii”, Trudy Belorusskogo gosudarstvennogo tekhnologicheskogo universiteta. Seriya 6, Fiziko-matematicheskie nauki i informatika, 13 (2005), 161–164
[6] M. Plonkowski, R. Urbanowicz, “Liczby podwojne i ich modyfikacje w neurokryptografii”, Przegląd Elektrotechniczny, 88(11b) (2002), 340–341
[7] Y. Choi, J. Sim, L-S. Kim, “CREMON: cryptography embedded on the convolutional neural network accelerator”, IEEE Transactions on Circuits and Systems II. Express Briefs, 67(12) (2020), 3337–3341 | DOI
[8] S. Jeong, C. Park, D. Hong, C. Seo, N. Jho, “Neural cryptography based on generalized tree parity machine for real-life systems”, Security and Communication Networks, 11 (2021), 1–12 | DOI
[9] A. Sarkar, “Neural cryptography using optimal structure of neural networks”, Applied Intelligence, 51 (2021), 8057–8066 | DOI
[10] S. Dourlens, Neuro-cryptographie appliquée et neuro-cryptanalyse du DES, University of Paris, Paris, 1995, 218 pp. | DOI
[11] A. Ruttor, Neural synchronization and cryptography [dissertation], Julius-Maximilians-Universitat Wurzburg, Wurzburg, 2006, +120 pp. | DOI
[12] M. Plonkovski, P. P. Urbanovich, “Sinkhronizatsiya kriptograficheskikh klyuchei na osnove neironnykh setei i v sistemakh kriptopreobrazovaniya na osnove XML”, Trudy Belorusskogo gosudarstvennogo tekhnologicheskogo universiteta. Seriya 6, Fiziko-matematicheskie nauki i informatika, 14 (2006), 152–155
[13] A. Ruttor, W. Kinzel, I. Kanter, “Dynamics of neural cryptography”, Physical Review E, 75(5) (2007), 056104 | DOI
[14] M. Dolecki, R. Kozera, “Distribution of the tree parity machine synchronization time”, Advances in Science and Technology, 7(18) (2013), 20–27 | DOI
[15] P. P. Urbanovich, K. V. Churikov, “Sravnitelnyi analiz metodov vzaimoobucheniya neironnykh setei v zadachakh obmena konfidentsialnoi informatsiei”, Trudy BGTU. Fiziko-matematicheskie nauki i informatika, 6 (2010), 163–166
[16] L. F. Seoane, A. Ruttor, “Successful attack on permutation-parity-machine-based neural cryptography”, Physical Review E, 85(2) (2012), 025101 | DOI
[17] L. N. Shacham, E. Klein, R. Mislovaty, I. Kanter, W. Kinzel, “Cooperating attackers in neural cryptography”, Physical Review E, 69(6) (2004), 066137 | DOI
[18] I. L. Kantor, A. S. Solodovnikov, Giperkompleksnye chisla, Nauka, Moskva, 1973, +144 pp.
[19] M. Płonkowski, R. Urbanowicz, E. Lisica, “Wykorzystanie kwaternionow w protokole uzgadniania klucza kryptograficznego, opartym na architekturach sieci neuronowych TPQM”, Przegląd Elektrotechniczny, 86(7) (2010), 90–91
[20] T. Dong, T. Huang, “Neural cryptography based on complex-valued neural network”, IEEE Transactions on Neural Networks and Learning Systems, 31(11) (2020), 4999–5004 | DOI
[21] Y. Zhang, W. Wang, H. Zhang, “Neural cryptography based on quaternion-valued neural network”, International Journal of Innovative Computing, Information and Control, 6(22) (2022), 1871–1883
[22] J. Wu, L. Xu, F. Wu, Y. Kong, L. Senhadji, H. Shu, “Deep octonion networks”, Neurocomputing, 397 (2019), 179–191 | DOI
[23] K. Takahashi, M. Fujita, M. Hashimoto, “Remarks on octonion-valued neural networks with application to robot manipulator control”, 2021 IEEE International Conference on Mechatronics (ICM) (Kashiwa, Japan), IEEE, 2021, 1–6 | DOI
[24] A. Cariow, G. Cariowa, “Fast algorithms for deep octonion networks”, IEEE Transactions on Neural Networks and Learning Systems, 34(1) (2023), 543–548 | DOI
[25] C. Ricotta, J. Podani, “On some properties of the Bray – Curtis dissimilarity and their ecological meaning”, Ecological Complexity, 31 (2017), 201–205 | DOI