Dynamic Key Generation Using GWO for IoT System

Balsam A. Hameedi - Ministry of Education-Iraq, Baghdad, Iraq
Muntaha A. Hatem - Ministry of Higher Education and Scientific Research-Iraq, Baghdad, Iraq
Jamal N. Hasoon - Mustansiriyah University -Iraq, Baghdad, Iraq

Citation Format:

DOI: http://dx.doi.org/10.62527/joiv.8.2.2761


One well-known technological advancement that significantly impacts many things is the Internet of Things (IoT). These include connectivity, work, healthcare, and the economy. IoT can improve life in many situations, including classrooms and smart cities, through work automation, increased output, and decreased worry. However, cyberattacks and other risks significantly impact intelligent Internet of Things applications. Key generation is essential in information security and the various applications that use a distributed system, networks, or Internet of Things (IoT) systems. Several algorithms have been developed to protect IoT applications from malicious attacks; since IoT devices usually have small memory resources and limited computing and power resources, traditional key generation methods are inappropriate because they require high computational power and memory usage. This paper proposes a method of Dynamic Key Generation Method (DKGM) to overcome the difficulty using a specific chaotic map called the Zaslavskii Map and a swarm intelligent algorithm for optimization called Grey Wolf Optimizer (GWO). DKGM's ability to generate several groups-seed numbers using the Zaslavskii map depends on various initial parameters. GWO selects strong generated numbers depending on the randomness test as a fitness function. Three wolfs GWα, GWβ, and GWΩ, are used to simulate the behavior of a pack of grey wolves when attacking prey. The speed and position of each wolf are updated depending on the best three wolves. Finally, use the sets GWα in the round, GWβ in the subkey, and GW in shifting operations of the Chacha20 hash function. The dynamic procedure was used to improve the high-security analysis of the DKGM approach over earlier methods. Simulations show that the suggested method is preferable for IoT applications.


Zaslavskii map; Grey Wolf Optimizer (GWO); chacha20 hash function; NIST test.

Full Text:



Z. Rahman, X. Yi, M. Billah, M. Sumi, and A. Anwar, "Enhancing AES Using Chaos and Logistic Map-Based Key Generation Technique for Securing IoT-Based Smart Home," Electronics, Vol.11, no.7, pp. 1083,2022.‏

Wax, J. Zhang, S. Huang, C. Luo, and W. Li, “Key generation for Internet of Things: a contemporary survey," ACM Computing Surveys (CSUR), vol.54, no.1, pp. 1-37,2021.

O. S. Guma’a, Q.M. Hussein, and Z. T. Mustafa," Dynamic keys generation for Internet of things," TELKOMNIKA Indonesian Journal of Electrical Engineering, vol.18, no.2, pp.4897-4909,2019

R. B Naik and U. Singh, "A Review on Applications of Chaotic Maps in Pseudorandom Number Generators and Encryption,” Annals of Data Science, pp.1-26,2022.

M. T. Taha and J.M. Al-Tuwaijari,"Improvement of Chacha20 Algorithm based on Tent and Chebyshev Chaotic Maps," Iraqi Journal of Science, pp.2029-2039,2021.‏

U. Zia, M. McCartney, B. Scotney, J. Martinez, and A. Sajjad,"A novel pseudorandom number generator for IoT based on a coupled map lattice system using the generalized symmetric map," SN Applied Sciences, vol.4, no.2,1-17,2022.

M. Kohli and S. Arora," Chaotic grey wolf optimization algorithm for constrained optimization problems," Journal of Computational Design and Engineering, vol.5, no.4, pp. 458-472,2018.‏

A.R. Kashani, M. Gandomi, C.V. Camp, and A.H. Gandomi,” Optimum design of shallow foundation using evolutionary algorithms," Soft Computing, vol.24, pp.6809-6833,2020.‏

D.Yang, G. Li, and G. Cheng,"On the efficiency of chaos optimization algorithms for global optimization Chaos, " Solitons & Fractals, vol.34, no.4, pp.1366-1375,2007.

G. Kaur and S. Arora, "Chaotic whale optimization algorithm," Journal of Computational Design and Engineering, vol. 5, no. 3, pp.275-284,2018.‏

J. R. Naif, G.H. Abdul-majeed, and A.K.& Farhan, "Internet of things security using the new chaotic system and lightweight AES, " Journal of Al-Qadisiyah for computer science and mathematics, vol.11, no.2, pp. 45-52,2019.‏

R. Vohra and B. Patel, "An efficient chaos-based optimization algorithm approach for cryptography," Communication Network Security, vol. 1, no.4, pp.75-79,2012.

Z. Rahman, X. Yi, I. Khalil, and M. Sumi, "Chaos and logistic map-based key generation technique for AES-driven IoT security," In International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness (pp. 177-193). Springer, Cham,2021.

R. A. Ali,"Random Number Generator based on Hybrid Algorithm between Particle Swarm Optimization (PSO) Algorithm and 3D-Chaotic System and its Application," Iraqi Journal of Information Technology. V, vl.8, no.3, 2018.‏

M.H. Ismael and A.T. Maolood, "Proposed Secure Key for Healthcare Platform," Iraqi Journal of Computers, Communications, CONTROL AND SYSTEMS ENGINEERING, vol.22, no.1,2022.

M. Khan and T. Shah" A novel construction of substitution box with Zaslavskii chaotic map and symmetric group," Journal of Intelligent & Fuzzy Systems, vol. 28, no.4, pp. 1509-1517,2015.

R. Hamza and F. Titouna,"A novel sensitive image encryption algorithm based on the Zaslavsky chaotic map," Information Security Journal: A Global Perspective, vol.25.no.4-6, pp. 162-179,2016.‏

N. Balaska, Z. Ahmida, A. Belmeguenai, and S. Boumerdassi, "Image encryption using a combination of Grain‐128a algorithm and Zaslavsky chaotic map," IET Image Processing, vol. 14, no.6, pp.1120-1131,2020.

S. Arunkumar and M. Krishnan, "Enhanced Audio Encryption using 2-D Zaslavsky Chaotic Map," In 2022 International Conference on Computer Communication and Informatics (ICCCI) (pp. 1-4). IEEE,2022.

Abdel-Basset, Mohamed, et al. "A new fusion of grey wolf optimizer algorithm with a two-phase mutation for feature selection." Expert Systems with Applications 139 (2020): 112824.

H. Faris, I., Aljarah, M.A. Al-Betar, and S. Mirjalili,"Grey wolf optimizer: a review of recent variants and applications, "Neural computing and applications, vol.30, no.2, pp.413-435,2018.‏

N.M. Hatta, A. M., Zain, R. Sallehuddin, Z. Shayfull, and Y. Yusoff, " Recent studies on optimization method of Grey Wolf Optimiser (GWO): a review (2014–2017), "Artificial Intelligence Review, vol. 52, no.4, pp.2651-2683, 2019.‏

Meidani, Kazem, et al. "Adaptive grey wolf optimizer." Neural Computing and Applications 34.10 (2022): 7711-7731.

Al-Tashi, Qasem, et al. "A review of grey wolf optimizer-based feature selection methods for classification." Evolutionary Machine Learning Techniques: Algorithms and Applications (2020): 273-286.

H. H. Alyas and A.A. Abdullah, "Enhancement the ChaCha20 Encryption Algorithm Based on Chaotic Maps," In Next Generation of Internet of Things (pp. 91-107). Springer, Singapore,2021.

Nadimi-Shahraki, Mohammad H., Shokooh Taghian, and Seyedali Mirjalili. "An improved grey wolf optimizer for solving engineering problems." Expert Systems with Applications 166 (2021): 113917.

Degabriele, Jean Paul, et al. "The security of chacha20-poly1305 in the multi-user setting." Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security. 2021.

J. N. Hasoon, B.A. Khalaf, R.S. Hameed, S.A. Mostafa, and A. H. Fadil, "A Lightweight Stream Ciphering Model Based on Chebyshev Chaotic Maps and One Dimensional Logistic," In International Conference on Advances in Cyber Security (pp. 35-46). Springer, Singapore,2021.

M. S. Mahdi, N.F. Hassan, and G.H. Abdul-Majeed, " An improved chacha algorithm for securing data on IoT devices," SN Applied Sciences, vol.3, no.4, pp.1-9,2021.‏

P. Yadav, I. Gupta, and S.K. Murthy, "Study and analysis of eSTREAM cipher Salsa and ChaCha," In 2016 IEEE International Conference on Engineering and Technology (ICETECH) (pp. 90-94), 2016.