Journal of Digital Information Management


Vol No. 18 ,Issue No. 4 2020

A Novel Algorithm for Generating pseudo Random Number
Gangyi Hu, Weili Kou, Sumeth Yuenyong, Jian Qu
College of Big Data and Intelligence Engineering, Southwest Forestry University, Kunming, China & Department of Computer Engineering, Faculty of Engineering, Mahidol University, Bangkok, Thailand & Department of Engineering technology, Panyapiwat Institu
Abstract: This paper proposes a pseudo random number generation algorithm based on cellular neural networks. It used the hyper-chaos characteristics of the cellular neural networks and sets the appropriate parameters to generate the pseudo random number. The experimental results show that, compared with other similar algorithms, this algorithm has the characteristics of simple operation, low complexity, large key space, and good randomness. It can meet the needs of secure communication and network information security, which has good application prospects.
Keywords: Cellular Neural Networks, Chaotic System, Pseudorandom Number A Novel Algorithm for Generating pseudo Random Number
DOI:https://doi.org/10.6025/jdim/2020/18/4/151-156
Full_Text   PDF 208 KB   Download:   232  times
References:

[1] Li Jetc. (2018). Efficient deterministic and nondeterministic pseudo-random number generation, Mathematics and Computers in Simulation. 1 (1) 143, 114 - 24.
[2] Bahi, J. M. (2017). An optimization technique on pseudo-random generators based on chaotic iterations, arXiv preprint arXiv, 27 (1) 1706 - 1713.
[3]Hamza, R. (2017). A novel pseudo random sequence generator for image-cryptographic applications, Journal of Information Security and Applications. 8 (1) 19-27.
[4] Hue, T. T. (2017). Complexity and properties of a multidimensional Cat-Hadamard map for pseudo random number generation, The European Physical Journal Special Topics. 226 (10) 2263-80.
[5] Sen, BAI. (2017). Method to generate the pseudo random sequence based on the statistical properties, Chinese Journal of Network and Information Security, 3 (1) 31-38.
[6] Li-hua, Dong. (2016). Method for generating pseudo random numbers based on cellular neural network, Journal of Communications, 37 (Z1) 85-91. 
[7] Shuang-shuang, Han. (2013). Generalized Synchronization theorem based chaotic pseudo random number genetrator and performance analysis, Applicaition Research of Computers, 30 (5) 1512-1514.
[8] Wang, X. (2013). Cryptanalysis of a parallel subimage encryption method with high-dimensionalchaos, Nonlinear Dynamics, 73 (1-2) 795 -800 .
[9] Qi, Y. B., Sun, K. H., Wang, H. H. (2015). The design and performance analysis of hyper-chaotic pseudo-random sequence generator, Computer Engineer and Applications. 53 (4) 135-139.
[10] Chua, L. O., Yang, L. (1988). Cellular neural networks: theory. IEEE Transactions on Circuits & Systems, 35 (10) 1257-1272.
[11] Rukhin, A., Soto, J., Nechvatal, J. (2010). A statistical test suite forrandom and pseudo-random number generators for cryptographic applications. Andrew Rukhin Juan Soto James Nechvatal Miles SmidElaine, 59 (4) 2289- 2297.
[12] WANG, Y. H. (2010). The design and applications of PRNG based on Henonmap with parameter perturbation. Journal of Chinese Information Processing, 59 (4) 2289- 2297.
[13] HOSSAIN, M. B., RAHMAN, M. T., RAHMAN, B. M. S. (2014). A new approach of image encryption using 3D chaotic map to enhance security of multimedia component.
International conference on Informatics, Electronics & Vision. 1-6.
[14] OJSE, Assad., Chetto, M. (2017). Design and analysis of two stream ciphers based on chaotic coupling and multiplexing techniques. Multimedia Tools Applications. 1 (6) 1–27
[15 ] Han D, Min L, Hao L ).A chaos robustness criterion for 2d piecewise smooth map with applications in pseudorandom number generator and image encryption with avalanche effect. Mathematical Problems in Engineering 10 (1) 1–14
[16] Huang, L., Shi, D., Gao, J. (2016). The design and its application in secure communication and image encryption of a new lorenz-like system with varying parameter. Mathematical Problems in Engineering, 1–11.
[17] Lin, M., Long, F., Guo, L . (2016). Grayscale image encryption based on latin square and cellular neural network. In: Control and Decision Conference (CCDC), 2016, IEEE, 2787–2793
[18] Runhe, Q., Zhu, C., Liu, S. (2015). A chaos image encryption algorithm based on binary sequence and baker mapping, International Industrial Informatics and Computer Engineering Conference (IIICEC 2015), Xi’an, China.