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Abstract

Smart grid systems necessitate robust data encryption to safeguard sensitive data. Hardware limitations and computational complexity, however, restrict the real-time applicability of encryption in certain smart grid environments. This investigation proposes a novel encryption algorithm, the Genetic Algorithm-Enhanced 8-Dimensional Chaotic System (GA-8D chaotic system), based on a complex chaotic system enhanced by a genetic algorithm, to secure smart grid data. The data was collected from a simulated smart grid environment, comprising real-time grid load data and sensor inputs. The proposed GA-8D chaotic system technique integrates a Genetic Algorithm (GA) and an 8D chaotic system for strong key generation and encryption. This approach enhances encryption speed and security by evolving optimal parameters using genetic algorithms, thereby ensuring higher performance in smart grid applications. The 8D chaotic system encryption technique is utilized to securely encrypt data and sensor images within the smart grid, guaranteeing data integrity and confidentiality within the smart grid architecture. The proposed algorithm demonstrated superior performance in encryption speed (0.1 ms), training accuracy (0.98), security strength (98%), and computational efficiency (450 bits). The results indicate that the algorithm is scalable for smart grid data encryption. The GA-Enhanced 8D chaotic system encryption algorithm successfully addresses the security challenges in smart grid data transmission.

Keywords

smart grid data encryption genetic algorithm-enhanced 8-Dimensional chaotic system (GA-8D-chaotic system)new complex chaotic system

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References

Pandiyan

Saravanan

Usha

, et al. Technological advancements toward smart energy management in smart cities. Energy Rep 2023; 10: 648–677. DOI: 10.1016/j.egyr.2023.07.021.

Achaal

Adda

Berger

, et al. Study of smart grid cyber-security, examining architectures, communication networks, cyber-attacks, countermeasure techniques, and challenges. Cybersecur 2024; 7(1): 10. DOI: 10.1186/s42400-023-00200-w.

Ramoliya

Trivedi

Darji

, et al. ML-based energy consumption and distribution framework analysis for EVs and charging stations in smart grid environment. IEEE Access 2024; 12: 23319–23337. DOI: 10.1109/ACCESS.2024.3365080.

Khan

Saleh

Waseem

, et al. Artificial intelligence enabled demand response: prospects and challenges in smart grid environment. IEEE Access 2022; 11: 1477–1505. DOI: 10.1109/ACCESS.2022.3231444.

Balu

Mukherjee

. Optimal allocation of electric vehicle charging stations and renewable distributed generation with battery energy storage in radial distribution system considering time sequence characteristics of generation and load demand. J Energy Storage 2023; 59: 106533. DOI: 10.1016/j.est.2022.106533.

Shakor

Khaleel

Safran

, et al. Dynamic aes encryption and blockchain key management: a novel solution for cloud data security. IEEE Access 2024; 12: 26334–26343. DOI: 10.1109/ACCESS.2024.3351119.

Tsantikidou

Sklavos

. Threats, Attacks, and cryptography frameworks of cybersecurity in critical infrastructures. Cryptography 2024; 8(1): 7. DOI: 10.3390/cryptography8010007.

Ahmed

Rehan

Basit

, et al. A dynamic optimal scheduling strategy for multi-charging scenarios of plug-in-electric vehicles over a smart grid. IEEE Access 2023; 11: 28992–29008. DOI: 10.1109/ACCESS.2023.3258859.

Deng

Wang

Lin

. Chaotic dynamical system of Hopfield neural network influenced by neuron activation threshold and its image encryption. Nonlinear Dyn 2024; 112(8): 6629–6646. DOI: 10.1007/s11071-024-09614-8.

10.

Sarfraz

Zhou

Ali

. An 8D hyperchaotic system of fractional-order systems using the memory effect of grünwald–letnikov derivatives. Fractal Fract 2024; 8(9): 530. DOI: 10.3390/fractalfract8090530.

11.

Zhang

Huang

Kou

, et al. Data encryption based on a 9D complex chaotic system with quaternion for smart grid. Chinese Phys B 2023; 32(1): 010502. DOI: 10.1088/1674-1056/ac76b2.

12.

Liu

Fang

Liu

. Chaotic systems-based secure communication scheme for detection of wind turbines. IEEE Trans Smart Grid 2023; 14(6): 4704–4716. DOI: 10.1109/TSG.2023.3250967.

13.

Huang

Zhang

Kou

, et al. A real-time image encryption algorithm for distributed energy system based on the 13D complex chaotic sequence. IEEE MultiMedia 2023; 30: 71–81. DOI: 10.1109/MMUL.2023.3317322.

14.

Clemente-Lopez

de Jesus Rangel-Magdaleno

Muñoz-Pacheco

. A lightweight chaos-based encryption scheme for IoT healthcare systems. Internet of Things 2024; 25: 101032. DOI: 10.1016/j.iot.2023.101032.

15.

Kifouche

Azzaz

Hamouche

, et al. Design and implementation of a new lightweight chaos-based cryptosystem to secure IoT communications. Int J Inf Secur 2022; 21(6): 1247–1262. DOI: 10.1007/s10207-022-00609-3.

16.

Youssef

Abdelli

Kharroubi

, et al. A secure chaos-based lightweight cryptosystem for the Internet of Things. IEEE Access 2023; 11: 123279–123294. DOI: 10.1109/ACCESS.2023.3326476.

17.

Soni

Thukral

Kanwar

. BCLM: a novel chaotic map for designing cryptography-based security mechanism for IEEE C37. 118.2 PMU communication in smart grid. Int J Emerg Elec Power Syst 2023; 29(0): 39–53. DOI: 10.1515/ijeeps-2023-0158.

18.

Wang

Zhang

Gao

, et al. A color image encryption algorithm based on hash table, hilbert curve and hyper-chaotic synchronization. Mathematics 2023; 11(3): 567. DOI: 10.3390/math11030567.

19.

Parthasarathy

, et al. A fast image encryption algorithm based on logistic mapping and hyperchaotic lorenz system for clear text correlation. IEEE Access 2023; 11: 91441–91453. DOI: 10.1109/ACCESS.2023.3305637.

20.

Liu

. An image autonomous selection encryption algorithm based on the delay exponential logistic chaotic model. Nonlinear Dyn 2024; 27: 1–22. DOI: 10.1007/s11071-024-09616-6.

21.

Zhang

. Image encryption algorithm based on a novel cascade chaotic system and DNA mutation. Phys Scr 2024; 99(10): 105203. DOI: 10.1088/1402-4896/ad6f48.

22.

Altameem

Poonia

, et al. A hybrid AES with a chaotic map-based biometric authentication framework for IoT and Industry 4.0. Systems 2023; 11(1): 28. DOI: 10.3390/systems11010028.

23.

Jin

Lin

, et al. A lightweight authentication scheme for power IoT based on PUF and Chebyshev chaotic map. IEEE Access 2024; 12: 83692–83706. DOI: 10.1109/ACCESS.2024.3413853.

24.

Wang

, et al. PSAK: a provably secure authenticated key agreement scheme based on extended Chebyshev chaotic maps for smart grid environments. Trans Emerging Tel Tech 2023; 34(5): e4752. DOI: 10.1002/ett.4752.

25.

Goel

Chawla

Dua

, et al. Novel hybrid chaotic map-based secure data transmission between smart meter and HAN devices. Smart Science 2024; 12(1): 27–42. DOI: 10.1080/23080477.2023.2264564.

26.

Wang

Nan

. Achieving light-weighted secure scheme for communication in a smart grid. IEEE Access 2022; 11: 14951–14960. DOI: 10.1109/ACCESS.2022.3228905.

27.

Murugan

Vijayarajan

. IoT based secured data monitoring system for renewable energy fed micro grid system. Sustain Energy Technol Assessments 2023; 57: 103244. DOI: 10.1016/j.seta.2023.103244.

Research on smart grid data encryption algorithm based on new complex chaotic system

Abstract

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