The article analyzes the use of artificial neural networks in modern cryptography and cryptoanalysis. The theoretical basis of artificial intelligence is briefly described. Sources for the use of artificial neural networks in block symmetric key encryption algorithms have been studied. As an example, the structure and operation of the SM4 block encryption algorithm are described in shortly. The components of artificial neurons and their computational processes are shown in graphs and formulas. Several scientific studies have been conducted on this topic. Three proposals have been put forward for the use of artificial neural networks in cryptanalysis.
Analysis Of The Use Of Artificial Neural Networks In Cryptanalysis Of Block Encryption Algorithms
DOI:
Keywords:
Cryptography
Block Cipher
SM4
Nonlinearity
Abstract
References
B. Abdurakhimov, I. Boykuziyev, and J. Abdurazzokov, “Encryption systems and the history of their development,” InterConf, no. 18(95), pp. 768–776, Jan. 2022, doi: 10.51582/interconf.19-20.01.2022.085.
B. Gonçalves, “The Turing Test is a Thought Experiment,” Minds Mach (Dordr), vol. 33, no. 1, pp. 1–31, Mar. 2023, doi: 10.1007/s11023-022-09616-8.
B. Abdurakhimov, J. Abdurazzokov, and L. Lingyun, “Analysis of the use of artificial neural networks in the cryptanalysis of the SM4 block encryption algorithm,” AIP Conf Proc, vol. 2812, no. 1, p. 020048, Aug. 2023, doi: 10.1063/5.0161859.
Bakhtiyor, B. Ilkhom, A. Javokhir, and A. Orif, “Using the Capabilities of Artificial Neural Networks in the Cryptanalysis of Symmetric Lightweight Block Ciphers,” 2024, pp. 113–121. doi: 10.1007/978-3-031-51521-7_16.
S.-W. Eum, H.-J. Kim, H.-D. Kwon, K.-B. Jang, H.-J. Kim, and H.-J. Seo, “Implementation of SM4 block cipher on CUDA GPU and its analysis,” in 2022 International Conference on Platform Technology and Service (PlatCon), IEEE, Aug. 2022, pp. 71–74. doi: 10.1109/PlatCon55845.2022.9932098.
N. A. Niyozmatova, N. S. Mamatov, B. I. Otaxonova, A. N. Samijonov, and K. K. Erejepov, “Classification Based On Decision Trees And Neural Networks,” in 2021 International Conference on Information Science and Communications Technologies (ICISCT), IEEE, Nov. 2021, pp. 01–04. doi: 10.1109/ICISCT52966.2021.9670345.
M. Uzair and N. Jamil, “Effects of Hidden Layers on the Efficiency of Neural networks,” in 2020 IEEE 23rd International Multitopic Conference (INMIC), IEEE, Nov. 2020, pp. 1–6. doi: 10.1109/INMIC50486.2020.9318195.
R. L. Rivest, “Cryptography and machine learning,” in Advances in Cryptology — ASIACRYPT ’91, H. Imai, R. L. Rivest, and T. Matsumoto, Eds., Berlin, Heidelberg: Springer Berlin Heidelberg, 1993, pp. 427–439.
W. Kinzel and I. Kanter, “Neural cryptography,” in Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP ’02., 2002, pp. 1351–1354 vol.3. doi: 10.1109/ICONIP.2002.1202841.
M. B. Albassal and A.-M. A. Wahdan, “Neural network based cryptanalysis of a feistel type block cipher,” in International Conference on Electrical, Electronic and Computer Engineering, 2004. ICEEC ’04., IEEE, pp. 231–237. doi: 10.1109/ICEEC.2004.1374430.
L. Lerman, G. Bontempi, and O. Markowitch, “A machine learning approach against a masked AES,” J Cryptogr Eng, vol. 5, no. 2, pp. 123–139, 2015, doi: 10.1007/s13389-014-0089-3.
D. Shen, Y. Song, Y. Lu, S. Long, and S. Tian, “Neural differential distinguishers for GIFT-128 and ASCON,” Journal of Information Security and Applications, vol. 82, p. 103758, May 2024, doi: 10.1016/j.jisa.2024.103758.
C. Brunetta and P. Picazo-Sanchez, “Modelling cryptographic distinguishers using machine learning,” J Cryptogr Eng, vol. 12, no. 2, pp. 123–135, Jun. 2022, doi: 10.1007/s13389-021-00262-x.
H. Kim et al., “Deep-Learning-Based Cryptanalysis of Lightweight Block Ciphers Revisited,” Entropy, vol. 25, no. 7, p. 986, Jun. 2023, doi: 10.3390/e25070986.
Y. Nozaki, S. Takemoto, Y. Ikezaki, and M. Yoshikawa, “Deep Learning Based Side-Channel Analysis for Lightweight Cipher PRESENT,” in 2023 15th International Conference on Computer and Automation Engineering (ICCAE), IEEE, Mar. 2023, pp. 570–574. doi: 10.1109/ICCAE56788.2023.10111444.