Abstract
The main goal of the research presented in this paper was to estimate the performance of applying neural networks trained with the usage of a chaotic model, that may serve as hashing functions. The Lorenz Attractor chaotic model was used for training data preparation, and Scaled Conjugate Gradient was used as a training algorithm. Networks consisted of two layers: a hidden layer with sigmoid neurons and an output layer with linear neurons. The method of bonding the input message with chaotic formula is presented. Created networks could return 256 or 512 bits of hash, however, this parameter can be easily adjusted before the training process. The performance analysis of networks is discussed (that is the time of hash computation) in comparison with popular standards SHA-256 and SHA-512 under the MATLAB environment. Further research may include analysis of networks’ training parameters (like mean squared error or gradient) or analysis of results of the statistical tests performed on networks output. The presented solution may be used as a security algorithm complementary to a certificated one (for example for additional data integrity checking).
Supported by the funds assigned by The Polish Ministry of Education and Science to Cracow University of Technology (J.T.) and to AGH University of Science and Technology (A.B.).
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References
Akhavan Masoumi, A., Samsudin, A., Akhshani, A.: A novel parallel hash function based on a 3D chaotic map. EURASIP J. Adv. Signal Process. 2013, 126 (2013). https://doi.org/10.1186/1687-6180-2013-126
Angela. Average file sizes. https://blog.online-convert.com/average-file-sizes/. Accessed Feb 2022
Bishop, C.M., Nasrabadi, N.M.: Pattern recognition and machine learning. Springer 4(4), 246–249 (2006)
Boeing, G.: Visual analysis of nonlinear dynamical systems: chaos, fractals, self-similarity and the limits of prediction. Systems 4(4) (2016). https://doi.org/10.3390/systems4040037
Gong, Z.: Survey on lightweight hash functions. J. Cryptol. Res. 3(1), 1–11 (2016)
Guesmi, R., Ben Farah, M.A., Kachouri, A., Samet, M.: Hash key-based image encryption using crossover operator and chaos. Multim. Tools Appl. 75(8), 4753–4769 (2015). https://doi.org/10.1007/s11042-015-2501-0
Khitish. Sha algorithms 160,224,256,384,512. https://nl.mathworks.com/matlabcentral/fileexchange/31795-sha-algorithms-160-224-256-384-512. Accessed Feb 2022
Kidoň, M., Dobai, R.: Evolutionary design of hash functions for IP address hashing using genetic programming. In: 2017 IEEE Congress on Evolutionary Computation (CEC), pp. 1720–1727 (2017). https://doi.org/10.1109/CEC.2017.7969509
Liu, H., Kadir, A., Liu, J.: Keyed hash function using hyper chaotic system with time-varying parameters perturbation. IEEE Access 7, 37211–37219 (2019). https://doi.org/10.1109/ACCESS.2019.2896661
Marco, A., Martinez, A., Bruno, O.: Fast, parallel and secure cryptography algorithm using lorenz’s attractor. Int. J. Mod. Phys. C 21 (2012). https://doi.org/10.1142/S0129183110015166
Medini, H., Sheikh, M., Murthy, D., Sathyanarayana, S., Patra, G.: Identical chaotic synchronization for hash generation. ACCENTS Trans. Inf. Secur. 2, 16–21 (2016). https://doi.org/10.19101/TIS.2017.25002
Møller, M.F.: A scaled conjugate gradient algorithm for fast supervised learning. Neural Netw. 6(4), 525–533 (1993). https://doi.org/10.1016/S0893-6080(05)80056-5
NIST. The secure hash algorithm validation system (SHAVS). Tech. rep. (2012). https://csrc.nist.gov/CSRC/media/Projects/Cryptographic-Algorithm-Validation-Program/documents/shs/SHAVS.pdf
NIST. Fips 180-4: Secure hash standard (SHS). Tech. rep. (2015). https://nvlpubs.nist.gov/nistpubs/FIPS/NIST.FIPS.180-4.pdf
NIST: Fips pub 202: Sha-3 standard: permutation-based hash and extendable-output functions. Tech. rep. (2015). https://nvlpubs.nist.gov/nistpubs/FIPS/NIST.FIPS.202.pdf
Peng, J., Jin, S.Z., Liu, H.I., Zhang, W.: A novel hash function based on hyperchaotic lorenz system. In: Cao, B., Li, T.F., Zhang, C.Y. (eds.) Fuzzy Information and Engineering, vol. 2, pp. 1529–1536. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-03664-4_162
Singh, M., Garg, D.: Choosing best hashing strategies and hash functions. In: 2009 IEEE International Advance Computing Conference, pp. 50–55 (2009). https://doi.org/10.1109/IADCC.2009.4808979
Süli, E., Mayers, D.F.: An Introduction to Numerical Analysis, pp. 328–329. Cambridge University Press, Cambridge (2003)
Tchórzewski, J., Jakóbik, A.: Theoretical and experimental analysis of cryptographic hash functions. J. Telecommun. Inf. Technol. (2019)
Tchórzewski, J., Jakóbik, A., Grzonka, D.: Towards ANN-based scalable hashing algorithm for secure task processing in computational clouds. In: 33rd European Conference on Modelling and Simulation, pp. 421–427 (2019)
Tchórzewski, J., Jakóbik, A., Iacono, M.: An ANN-based scalable hashing algorithm for computational clouds with schedulers. Int. J. Appl. Math. Comput. Sci. 31(4), 697–712 (2021)
Wang, J., Zhang, T., Song, J., Sebe, N., Shen, H.T.: A survey on learning to hash. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 769–790 (2018). https://doi.org/10.1109/TPAMI.2017.2699960
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Tchórzewski, J., Byrski, A. (2022). Performance of Computing Hash-Codes with Chaotically-Trained Artificial Neural Networks. In: Groen, D., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2022. ICCS 2022. Lecture Notes in Computer Science, vol 13351. Springer, Cham. https://doi.org/10.1007/978-3-031-08754-7_48
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