Reinforcement Learning-Based Chaotic Communication System for Secure Transmission of Encrypted State Information in the Smart Grid

Document Type : Research Paper

Authors

Azarbaijan Shahid Madani University, Tabriz, Iran.

Abstract

This paper proposes a system for transmitting and receiving encrypted state information via a communication channel in the presence of noise and interference. The proposed system uses a chaotic signal generated by a Lorentz oscillator to encrypt the state information, which is then transmitted through the communication channel. At the receiver end, the received signal is decrypted using a similar key generated by a forced Lorentz oscillator. The accurate determination of the force signal is essential for synchronizing chaotic signals, and this paper proposes the use of reinforcement deep learning agents to train and determine the force signal. The proposed communication scheme involves the use of a state estimator, a master chaotic oscillator, two slave oscillators, and two RL agents. The proposed system was simulated using MATLAB Simulink, and the results show that the errors exhibit a repetitive nature, with low and high values corresponding to the input signal. The proposed system provides a reliable and secure system for transmitting sensitive information over communication channels.

Keywords

Main Subjects

References

  1. M. K. Hasan, A.A. Habib, Z. Shukur, F. Ibrahim, S. Islam, M.A. Razzaque, Review on the cyber-physical and cyber-security system in smart grid: Standards, protocols, constraints, and recommendations, Journal of Network and Computer Applications, vol. 209, p. 103540, Jan. 2023.
  2. M. K. Hasan, A.A. Habib, S. Islam, M. Balfaqih, K.M. Alfawaz, and Singh, Smart Grid Communication Networks for Electric Vehicles Empowering Distributed Energy Generation: Constraints, Challenges, and Recommendations, Energies, vol. 16, no. 3, p. 1140, Jan. 2023.
  3. X. Xiang and J. Cao, An efficient authenticated key agreement scheme supporting privacy-preservation for smart grid communication panel, Electric Power Systems Research, vol. 203, p. 107630, Feb. 2022.
  4. P. Haji Mirzaee, M. Shojafar, H. Cruickshank, and R. Tafazolli, ”Smart Grid Security and Privacy: From Conventional to Machine Learning Issues (Threats and Countermeasures),” IEEE Access, vol. 10, pp. 52922- 52954, May 2022.
  5. Y. Kim, S. Hakak, and A. Ghorbani, ”Smart grid security: Attacks and defense techniques,” IET Smart Grid, vol. 6, no. 2, pp. 103-123, Apr. 2023.
  6. M. Zhang, C. Shen, N. He, S. Han, Q. Li, Q. Wang, and X. Guan, ”False data injection attacks against smart grid state estimation: Construction, detection and defense,” Science China Technological Sciences, vol. 62, no. 11, pp. 2085-2102, Sep. 2019.
  7. T. Nguyen, S. Wang, M. Alhazmi, M. Nazemi, A. Estebsari, and P. Dehghanian, ”Electric Power Grid Resilience to Cyber Adversaries: State of the Art,” IEEE Access, vol. 8, pp. 86905-86922, May 2020, doi: 10.1109/ACCESS.2020.2993233.
  8. Linge Sagar Gajanan, Mukesh Kirar, and More Raju, ”Wide area Monitoring and Protection in Cyber-Physical Infrastructure,” published in 2023 IEEE Renewable Energy and Sustainable E- E-Mobility Conference (RESEM), Bhopal, India, 17-18 May 2023, 1-6.
  9. Gang Cheng,  Yuzhang  Lin,  Ali  Abur,  Antonio  Go´mez-Expo´sito,  and Wenchuan Wu, ”A Survey of Power System State Estimation Using Multiple Data Sources: PMUs, SCADA, AMI, and Beyond,” IEEE Transactions on Smart Grid, 14, no. 3, pp. 1738-1753, May 2023.
  10. L. Tightiz and H. Yang, ”A Comprehensive Review on IoT Protocols’ Features in Smart Grid Communication,” Energies, vol. 13, no. 11, p. 2762, Jun. 2020, doi: 10.3390/en13112762.
  11. D. Yan, F. Liu, Y. Zhang, and K. Jia, ”Dynamical model for individual defense against cyber epidemic attacks,” IET Information Security, vol. 13, no. 3, pp. 541-551, May 2019.
  12. B. M.R. Amin, S. Taghizadeh, M.S. Rahman, M.J. Hossain, V. Varad-Karajan, and Z. Chen, ”Cyber attacks in smart grid – dynamic impacts, analyses, and recommendations,” IET Cyber-Physical Systems: Theory & Applications, vol. 5, no. 4, pp. 321-329, Jul. 2020.
  13. D. He, H. Wang, M. K. Khan, and L. Wang, ”Lightweight anonymous key distribution scheme for smart grid using elliptic curve cryptography,” IET Communications, vol. 10, no. 14, pp. 1795-1802, Sep. 2016.
  14. M. R. Asghar, G. Da´n, D. Miorandi, and I. Chlamtac, ”Smart Meter Data Privacy: A Survey,” IEEE Communications Surveys & Tutorials, vol. 19, no. 4, pp. 2820-2835, Fourth quarter 2017.
  15. K. Sun, Chaotic Secure Communication: Principles and Technologies. Berlin, Germany: De Gruyter, 2019.
  16. K. Pyragas, ”Delayed feedback control of chaos,” Phil. Trans. R. Soc. A, vol. 364, pp. 2309-2334, 2006. doi:10.1098/rsta.2006.1827.
  17. J. C. Claussen, ”Floquet stability analysis of Ott-Grebogi-Yorke and difference control,” New Journal of Physics, vol. 10, no. 6, pp. 063006 (13pp), June 2008.
  18. S. Chen and J.  Lu¨,  ”Synchronization of an uncertain unified chaotic system via adaptive control,” Chaos, Solitons & Fractals, vol. 14, no. 4, pp. 643-647, Sep. 2002. doi:10.1016/S0960-0779(01)00255-1.
  19. Bo-Wen Shen, ”A Review of Lorenz’s Models from 1960 to 2008,” International Journal of Bifurcation and Chaos, 33, no. 10, article no. 2330024, October 2023. doi: 10.1142/S021812742330024X.
  20. Louis M. Pecora and Thomas L. Carroll, ”Synchronization in chaotic systems,” Physical Review Letters, vol. 64, no. 8, pp. 821-824, February doi: 10.1103/PhysRevLett.64.821.
  21. Yutaka Matsuo, Yann LeCun, Maneesh Sahani, Doina Precup, David Silver, Masashi Sugiyama, Eiji Uchibe, and Jun Morimoto, ”Deep learn- ing, reinforcement learning, and world models,” Neural Networks, vol. 152, pp. 267-275, August 2022, Special Issue on AI and Brain Science: Perspective.
  22. Jesse Clifton and Eric Laber, ”Q-Learning: Theory and Applications,” Annual Review of Statistics and Its Application, vol. 7, pp. 279-301, 2020.
  23. B. Ramadevi and K. Bingi, ”Chaotic Time Series Forecasting Ap- proaches Using Machine Learning Techniques: A Review,” Symmetry, vol. 14, no. 5, p. 955, May 2022. doi: 10.3390/sym14050955.
  24. C. T. Lin and C.P. Jou, ”Controlling chaos by GA-based reinforcement learning neural network,” IEEE Trans. Neural Netw., vol. 10, pp. 846-859, 1999.

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History

  • Receive Date: 17 August 2024
  • Accept Date: 05 February 2025

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