Chaotic Map based Random Binary Key Sequence Generation
Автор: Vishwas C.G.M., R. Sanjeev Kunte
Журнал: International Journal of Computer Network and Information Security @ijcnis
Статья в выпуске: 4 vol.16, 2024 года.
Бесплатный доступ
Image encryption is an efficient mechanism by which digital images can be secured during transmission over communication in which key sequence generation plays a vital role. The proposed system consists of stages such as the generation of four chaotic maps, conversion of generated maps to binary vectors, rotation of Linear Feedback Shift Register (LFSR), and selection of generated binary chaotic key sequences from the generated key pool. The novelty of this implementation is to generate binary sequences by selecting from all four chaotic maps viz., Tent, Logistic, Henon, and Arnold Cat map (ACM). LFSR selects chaotic maps to produce random key sequences. Five primitive polynomials of degrees 5, 6, 7, and 8 are considered for the generation of key sequences. Each primitive polynomial generates 61 binary key sequences stored in a binary key pool. All 61 binary key sequences generated are submitted for the NIST and FIPS tests. Performance analysis is carried out of the generated binary key sequences. From the obtained results, it can be concluded that the binary key sequences are random and unpredictable and have a large key space based on the individual and combination of key sequences. Also, the generated binary key sequences can be efficiently utilized for the encryption of digital images.
Linear Feedback Shift Register, Binary, Chaotic Map, Chaotic Random Binary Key Sequence, Binary Key Pool
Короткий адрес: https://sciup.org/15019298
IDR: 15019298 | DOI: 10.5815/ijcnis.2024.04.07
Список литературы Chaotic Map based Random Binary Key Sequence Generation
- Liu, L., Hu, H., Deng, Y., & Miao, S., “Pseudorandom bit generator based on non-stationary logistic maps,” IET Information Security, Vol. 10(2), pp. 87–94, 2016.
- Yu F, Li L, Tang Q, Cai S, Song Y, Xu Q, “A Survey on True Random Number Generators Based on Chaos,” Discrete Dynamics in Nature and Society, pp. 1-10, 2019.
- Shu-Bo, L., Jing, S., Zheng-Quan, X., Jin-Shuo L., “Digital chaotic sequence generator based on coupled chaotic systems,” Chinese Physics B, Vol. 18(12), pp. 5219–5227, 2009.
- Narendra K Pareek, Vinod Patidar, and Krishan K Sud, “A Random Bit Generator Using Chaotic Maps,” International Journal of Network Security, Vol.10, No.1, pp. 32–38, 2010.
- Hamed Rahimov, Majid Babaei, Mohsen Farhadi, “Cryptographic PRNG Based on Combination of LFSR and Chaotic Logistic Map,” Applied Mathametics, Vol. 2, pp. 1531-1534, 2011.
- Hu, H., Liu, L., Ding, N., “Pseudorandom sequence generator based on the Chen chaotic system,” Computer Physics Communications, Vol. 184(3), pp. 765–768, 2013.
- Huang F. & Zhao Y. “New key-stream generation scheme based on Henon chaotic system,” Journal of Central South University, Vol. 20(7), pp. 1904–1908, 2013.
- Ahmad, M., Doja, M.N., Beg, M.M.S., “A New Chaotic Map Based Secure and Efficient Pseudo-Random Bit Sequence Generation,” Security in Computing and Communications Vol. 969, pp 543-553, 2019.
- Yousif, A, and Kashmar A. H, “Key Generator to Encryption Images Based on Chaotic Maps,” Iraqi Journal of Science, Vol. 60(2), pp. 362–370, 2019.
- Wang, L., & Cheng, H., “Pseudo-Random Number Generator Based on Logistic Chaotic System,” Entropy, Vol. 21(10), 960, pp. 1-12. 2019.
- Ansam Sabah Bader, Shaymaa Hameed and Maisa’a Abid Ali K, “Key Generation based on Henon map and Lorenz System,” Al-Mustansiriyah Journal of Sciences, Vol. 31, pp. 41-46, 2020.
- Rahman, Z., Yi, X., Khalil, I., Sumi, M., “Chaos and Logistic Map Based Key Generation Technique for AES-Driven IoT Security,” Quality, Reliability, Security, and Robustness in Heterogeneous Systems. QShine, 2021.
- Li C., Luo, G., Qin., K, and Li C., “An image encryption scheme based on chaotic tent map,” Nonlinear Dynamics, Vol. 87, pp. 127–133, 2019.
- Vishwas C. G. M., R. Sanjeev Kunte, Varun Yarehalli Chandrappa, "Encryption Using Binary Key Sequences in Chaotic Cryptosystem", International Journal of Computer Network and Information Security, Vol. 15, No.4, pp. 48-60, 2023.
- M. V. Mandi, K. Haribhat, and R. Murali, “Generation of a large set of binary sequences derived from chaotic functions with large linear complexity and good cross correlation properties,” International Journal of Advanced Engineering Applications, Vol. 3, pp. 313–322, 2010.
- D. Malchev and I. Ibryam, “Construction of pseudorandom binary sequences using chaotic maps,” Applied Mathematical Sciences, Vol. 9, no. 78, pp. 3847–3853, 2015.
- C. Fatima and D. Ali, “New chaotic binary sequences with good correlation property using logistic maps?” IOSR Journal of Electronics and Communication Engineering, Vol. 5, no. 3, pp. 59–64, 2013.
- L. Merah, A.-P. Adda, and H.-s. Naima, “Enhanced chaos-based pseudo random numbers generator,” International Conference on Applied Smart Systems, pp. 1–7, 2018,
- L. E. Bassham III, A. L. Rukhin, J. Soto, J. R. Nechvatal, M. E. Smid, E. B. Barker, S. D. Leigh, M. Levenson, M. Vangel, D. L. Banks et.al., “SP 800-22 rev. 1a. A statistical test suite for random and pseudorandom number generators for cryptographic applications,” 2010.
- D. Hurley-Smith, C. Patsakis, and J. Hernandez-Castro, “On the unbearable lightness of fips 140-2 randomness tests,” IEEE Transactions on Information Forensics and Security, 2020.
- Huang, F., & Zhao, Y.), “New key-stream generation scheme based on Hénon chaotic system,” Journal of Central South University, Vol. 20(7), pp. 1904–1908, 2013.
- Pirbhulal S, Zhang H, Wu W, Mukhopadhyay SC, Zhang Y-T., “Heartbeats based biometric random binary sequences generation to secure wireless body sensor networks,” IEEE Transactions on Biomedical Engineering”, Vol. 65(12), pp. 2751-2759, 2018.
- Alawida M, Samsudin A, Teh J S., and Alshoura W. H, “Digital cosine chaotic map for cryptographic applications,” IEEE Access. Vol. 7, pp. 150609-150622, 2019.
- Huang X, Liu L, Li X, Yu M,Wu Z, “A new two-dimensional mutual coupled logistic map and its application for pseudorandom number generator”, Mathematical Problems in Engineering, pp. 1-10, 2019.
- Wu W, Pirbhulal S, Li G. Adaptive computing-based biometric security for intelligent medical applications. Neural Computing and Applications. Vol. 32, pp. 11055-11064, 2020.
- Siddaramanna S, Sarapady Venkatramanayya S., “Generation of chaotic random binary sequences for cryptographic applications,” Concurrency Computation: Practice and Experience, e6497, 2021.
- Norouzi, B., Seyedzadeh, S.M., Mirzakuchaki, S., Mosavi M. R, “A novel image encryption based on row-column, masking and main diffusion processes with hyper chaos,” Multimedia Tools and Applications, Vol. 74, pp. 781–811, 2015.
- Borujeni S. E, Eshghi M, “Chaotic image encryption system using phase-magnitude transformation and pixel substitution,” Telecommunication Systems, Vol. 52, pp. 525–537, 2013.
- Zhou N, Zhang A, Zheng F, Gong L, “Novel image compression–encryption hybrid algorithm based on key-controlled measurement matrix in compressive sensing,” Optics and Laser Technologies, Vol. 62(2), pp. 152–160, 2014.
- Li Y, Wang C, Chen H, “A hyper-chaos-based image encryption algorithm using pixel-level permutation and bit-level permutation,” Optics and Lasers in Engineering, Vol. 90, pp. 238–246, 2017.
- M. K, Nalini and K.R, Radhika, “Secured Key Generation for Biometric Encryption using Hyper-chaotic Map and DNA Sequences,” ICICNIS 2020, http://dx.doi.org/10.2139/ssrn.3769813, 2021.
- Siddaramanna S, Sarapady Venkatramanayya S, “Key Sequences based on Cyclic Elliptic Curves over GF(28) with Logistic Map for Cryptographic Applications,” Concurrency Computation: Practice and Experience, 2022.
