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Cyber-Physical Security in Smart Grids: A Holistic View with Machine Learning Integration

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Machine Learning for Cyber Physical System: Advances and Challenges

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 60))

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Abstract

Cyber-physical attacks are become more challenging in each passing days owing to the continuous advancement of smart-grid systems. In the present industrial revolution, the smart grid is integrated with a wide-range of technologies, equipment/devices and tools/software to make the system more trustworthy, reliable, efficient, and cost-effective. Regardless of achieving these objectives, the peril area for the critical attacks has also been stretched owing to the add-on cyber-layers. In order to detect and mitigate these attacks, the machine learning (ML) tools are being reliably and massively used. In this chapter, the authors have reviewed several state-of-the-art related researches comprehensively. The advantages and disadvantages of each ML based schemes are identified and reported in this chapter. Finally, the authors have presented the shortcomings of the existing researches and possible future research direction based on their investigation.

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References

  1. https://www.iea.org/energy-system/electricity/smart-grids [Accessed on June 28 2023]

  2. https://www.nsgm.gov.in/en/smart-grid [Accessed on July 12, 2023]

  3. Kim, Y., Hakak, S., Ghorbani, A.: Smart grid security: Attacks and defence techniques. IET Smart Grid 6(2), 103–123 (2023)

    Article  Google Scholar 

  4. Canadian Institute for Cybersecurity (CIC): Operational Technology (OT) Forensics, pp. 1–141. University of New Brunswick (2019)

    Google Scholar 

  5. Stouffer, K., Falco, J., Scarfone, K.: Guide to Industrial Control Systems (ICS) Security (No. NIST Special Publication (SP) 800‐82 (Retired Draft)). National Institute of Standards and Technology

    Google Scholar 

  6. Wang, Y., et al.: Analysis of smart grid security standards. In: Proc. Int. Conf. Computer Science and Automation Engineering, Shanghai, China, June 2011, pp. 697–701

    Google Scholar 

  7. https://www.nwkings.com/objectives-of-cyber-security [Accessed on July 15, 2023]

  8. https://sprinto.com/blog/cyber-security-goals/# What_are_Cyber_Security _Goals_or_Objectives [Accessed on July 18, 2023]

  9. Chen, B., Wang, J., Shahidehpour, M.: Cyber–physical perspective on smart grid design and operation. IET Cyber-Physical Systems: Theory & Applications 3(3), 129–141 (2018)

    Article  Google Scholar 

  10. Guo, Q., Hiskens, I., Jin, D., Su, W., Zhang, L.: Editorial: cyberphysical Systems in Smart Grids: security and operation. IET Cyber-Physical Systems: Theory & Applications 2(4), 153–154 (2017)

    Article  Google Scholar 

  11. Patnaik, B., Mishra, M., Bansal, R.C., Jena, R.K.: AC microgrid protection–A review: Current and future prospective. Appl. Energy 271, 115210 (2020)

    Article  Google Scholar 

  12. Mishra, M., Patnaik, B., Biswal, M., Hasan, S., Bansal, R.C.: A systematic review on DC-microgrid protection and grounding techniques: Issues, challenges and future perspective. Appl. Energy 313, 118810 (2022)

    Article  Google Scholar 

  13. Spring, J. M., Fallon, J., Galyardt, A., Horneman, A., Metcalf, L., & Stoner, E. (2019). Machine Learning in Cybersecurity: A Guide. SEI Carnegie Mellon Technical Report CMU/SEI-2019-TR-005.

    Google Scholar 

  14. Apruzzese, G., Laskov, P., Montes de Oca, E., Mallouli, W., Brdalo Rapa, L., Grammatopoulos, A.V., Di Franco, F.: The role of machine learning in cybersecurity. Digital Threats: Research and Practice 4(1), 1–38 (2023)

    Article  Google Scholar 

  15. Giovanni Apruzzese, Michele Colajanni, Luca Ferretti, Alessandro Guido, and Mirco Marchetti. 2018. On the effectiveness of machine and deep learning for cybersecurity. In Proceedings of the IEEE International Conference on Cyber Conflicts. 371–390.

    Google Scholar 

  16. Giovanni Apruzzese, Michele Colajanni, Luca Ferretti, and Mirco Marchetti. 2019. Addressing adversarial attacks against security systems based on machine learning. In Proceedings of the IEEE International Conference on Cyber Conflicts. 1–18.

    Google Scholar 

  17. Kasun Amarasinghe, Kevin Kenney, and Milos Manic. 2018. Toward explainable deep neural network based anomaly detection. In Proceedings of the IEEE International Conference Human System Interaction. 311–317.

    Google Scholar 

  18. Baig, Z.A., Amoudi, A.R.: An analysis of smart grid attacks and countermeasures. J. Commun. 8(8), 473–479 (2013). https://doi.org/10. 12720/jcm.8.8.473‐479

  19. Bou‐Harb, E., et al.: Communication security for smart grid distribution networks. IEEE Commun. Mag. 51(1), 42–49 (2013). https://doi.org/10. 1109/mcom.2013.6400437

  20. Hansen, A., Staggs, J., Shenoi, S.: Security analysis of an advanced metering infrastructure. Int. J. Crit. Infrastruct. Protect. 18, 3–19 (2017). https://doi.org/10.1016/j.ijcip.2017.03.004

    Article  Google Scholar 

  21. Wang, K., et al.: Strategic honeypot game model for distributed denial of service attacks in smart grid. IEEE Trans. Smart Grid. 8(5), 2474–2482 (2017). https://doi.org/10.1109/tsg.2017.2670144

    Article  Google Scholar 

  22. Farraj, A., Hammad, E., Kundur, D.: A distributed control paradigm for smart grid to address attacks on data integrity and availability. IEEE Trans. Signal Inf. Process. Netw. 4(1), 70–81 (2017). https://doi.org/10. 1109/tsipn.2017.2723762

  23. Chen, P.Y., Cheng, S.M., Chen, K.C.: Smart attacks in smart grid communication networks. IEEE Commun. Mag.Commun. Mag. 50(8), 24–29 (2012). https://doi.org/10.1109/mcom.2012.6257523

    Article  Google Scholar 

  24. Sanjab, A., et al.: Smart grid security: threats, challenges, and solutions. arXiv preprint arXiv: 1606.06992

    Google Scholar 

  25. Liu, S.Z., Li, Y.F., Yang, Z.: Modeling of cyber‐attacks and defenses in local metering system. Energy Proc. 145, 421–426 (2018). https://doi.org/10.1016/j.egypro.2018.04.069

  26. Sun, C.C., et al.: Intrusion detection for cybersecurity of smart meters. IEEE Trans. Smart Grid. 12(1), 612–622 (2020). https://doi.org/10. 1109/tsg.2020.3010230

  27. Bansal, G., Naren, N., Chamola, V.: RAMA: real‐time automobile mutual authentication protocol using PUF. In: Proc. Int. Conf. Cloud Computing Environment Based on Game Theory, Barcelona, Spain, January 2020, pp. 265–270

    Google Scholar 

  28. Bhattacharjee, S., et al.: Statistical security incident forensics against data falsification in smart grid advanced metering infrastructure. In: Proc. Int. Conf. Data and Application Security and Privacy, Scottsdale, USA, March 2017, pp. 35–45

    Google Scholar 

  29. Wei, L., et al.: Stochastic games for power grid protection against co-ordinated cyber-physical attacks. IEEE Trans. Smart Grid. 9(2), 684–694 (2018). https://doi.org/10.1109/tsg.2016.2561266

    Article  Google Scholar 

  30. “Shodan,” https://www.shodan.io/. [Accessed on August 8, 2023]

  31. Mashima, D., Li, Y., & Chen, B. (2019, December). Who's scanning our smart grid? empirical study on honeypot data. In 2019 IEEE Global Communications Conference (GLOBECOM) (pp. 1–6). IEEE.

    Google Scholar 

  32. Liu, N., et al.: A key management scheme for secure communications of advanced metering infrastructure in smart grid. IEEE Trans. Ind. Electron. 60(10), 4746–4756 (2012). https://doi.org/10.1109/tie.2012.2216237

    Article  Google Scholar 

  33. Liu, X., et al.: A collaborative intrusion detection mechanism against false data injection attack in advanced metering infrastructure. IEEE Trans. Smart Grid. 6(5), 2435–2443 (2015). https://doi.org/10.1109/tsg.2015.2418280

    Article  Google Scholar 

  34. Lee, S.: Security and privacy protection of vehicle-to-grid technology for electric vehicle in smart grid environment. J. Convergence Culture Technol. 6(1), 441–448 (2020)

    Google Scholar 

  35. Park, K.S., Yoon, D.G., Noh, S.: A secure authentication and key agreement scheme for smart grid environments without tamper-resistant devices. J. Korea Inst. Inf. Secur. Cryptol. 30(3), 313–323 (2020)

    Google Scholar 

  36. Kaveh, M., Martín, D., Mosavi, M.R.: A lightweight authentication scheme for V2G communications: a PUF-based approach ensuring cyber/physical security and identity/location privacy. Electronics 9(9), 1479 (2020). https://doi.org/10.3390/electronics9091479

    Article  Google Scholar 

  37. Zhang, L., et al.: A lightweight authentication scheme with privacy protection for Smart Grid communications. Future Generat. Comput. Syst. 100, 770–778 (2019). https://doi.org/10.1016/j.future.2019.05.069

    Article  Google Scholar 

  38. Go, Y.M., Kwon, K.H.: Countermeasure of SIP impersonation attack using a location server. J. Korea Contents Assoc. 13(4), 17–22 (2013). https://doi.org/10.5392/jkca.2013.13.04.017

    Article  Google Scholar 

  39. Roberts, B., et al.: An authentication framework for electric vehicle‐to‐ electric vehicle charging applications. In: Proc. Int. Conf. Mobile Ad Hoc and Sensor Systems, Orlando, USA, November 2017, pp. 565–569

    Google Scholar 

  40. Guo, Z., et al.: Time synchronization attack and countermeasure for multisystem scheduling in remote estimation. IEEE Trans. Automat. Control. 66(2), 916–923 (2020). https://doi.org/10.1109/tac.2020.2997318

    Article  MathSciNet  Google Scholar 

  41. Chan, A.C.F., Zhou, J.: A secure, intelligent electric vehicle ecosystem for safe integration with smart grid. IEEE Trans. Intell. Transport. Syst. 16(6), 3367–3376 (2015). https://doi.org/10.1109/tits.2015.2449307

    Article  Google Scholar 

  42. Kakei, S., et al.: Cross-certification towards distributed authentication infrastructure: a case of hyperledger fabric. IEEE Access. 8, 135742–135757 (2020). https://doi.org/10.1109/access.2020.3011137

    Article  Google Scholar 

  43. Li, Q., et al.: A risk assessment method of smart grid in cloud computing environment based on game theory. In: Proc. Int. Conf. Cloud Computing and Big Data Analytics, Chengdu, China, April 2020, pp. 67–72

    Google Scholar 

  44. Shen, S., Tang, S.: Cross‐domain grid authentication and authorization scheme based on trust management and delegation. In: Proc. Int. Conf. Computational Intelligence and Security, Suzhou, China, December 2008, pp. 399–404

    Google Scholar 

  45. Chu, Z., et al.: Game theory based secure wireless powered D2D communications with cooperative jamming. In: Proc. Int. Conf. Wireless Days, Porto, Portugal, March 2017, pp. 95–98

    Google Scholar 

  46. Pawlick, J., Zhu, Q.: Proactive defense against physical denial of service attacks using Poisson signaling games. In: International Conference on Decision and Game Theory for Security, October 2017, pp. 336–356. Springer, Cham

    Google Scholar 

  47. Lu, Z., et al.: Review and evaluation of security threats on the communication networks in smart grid. In: Proc. Int. Conf. Military Communications, San Jose, USA

    Google Scholar 

  48. Hewett, R., Rudrapattana, S., Kijsanayothin, P.: Cyber‐security analysis of smart grid SCADA systems with game models. In: Proc. Int. Conf. Cyber and Information Security Research, New York, USA, April 2014, pp. 109–112

    Google Scholar 

  49. Pan, K., et al.: Combined data integrity and availability attacks on state estimation in cyber‐physical power grids. In: Proc. Int. Conf. Smart Grid Communications, Sydney, Australia, November 2016, pp. 271–277

    Google Scholar 

  50. Jeong, Y.S.: Probability-based IoT management model using blockchain to expand multilayered networks. J. Korea Convergence Soc. 11(4), 33–39 (2020)

    Google Scholar 

  51. Wang, D., Wang, X., Zhang, Y., Jin, L.: Detection of power grid disturbances and cyber-attacks based on machine learning. Journal of information security and applications 46, 42–52 (2019)

    Article  Google Scholar 

  52. Vijayanand, R., Devaraj, D., & Kannapiran, B. (2019, April). A novel deep learning based intrusion detection system for smart meter communication network. In 2019 IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS) (pp. 1–3). IEEE.

    Google Scholar 

  53. Zhou, L., Ouyang, X., Ying, H., Han, L., Cheng, Y., & Zhang, T. (2018, October). Cyber-attack classification in smart grid via deep neural network. In Proceedings of the 2nd international conference on computer science and application engineering (pp. 1–5).

    Google Scholar 

  54. Niu, X., Li, J., Sun, J., & Tomsovic, K. (2019, February). Dynamic detection of false data injection attack in smart grid using deep learning. In 2019 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT) (pp. 1–6). IEEE.

    Google Scholar 

  55. Mohammadpourfard, M., Genc, I., Lakshminarayana, S., & Konstantinou, C. (2021, October). Attack detection and localization in smart grid with image-based deep learning. In 2021 IEEE international conference on communications, control, and computing technologies for smart grids (SmartGridComm) (pp. 121–126). IEEE.

    Google Scholar 

  56. Farrukh, Y. A., Ahmad, Z., Khan, I., & Elavarasan, R. M. (2021, November). A sequential supervised machine learning approach for cyber attack detection in a smart grid system. In 2021 North American Power Symposium (NAPS) (pp. 1–6). IEEE.

    Google Scholar 

  57. Sakhnini, J., Karimipour, H., Dehghantanha, A., Parizi, R.M.: Physical layer attack identification and localization in cyber–physical grid: An ensemble deep learning based approach. Physical Communication 47, 101394 (2021)

    Article  Google Scholar 

  58. Kurt, M.N., Ogundijo, O., Li, C., Wang, X.: Online cyber-attack detection in smart grid: A reinforcement learning approach. IEEE Transactions on Smart Grid 10(5), 5174–5185 (2018)

    Article  Google Scholar 

  59. Siniosoglou, I., Radoglou-Grammatikis, P., Efstathopoulos, G., Fouliras, P., Sarigiannidis, P.: A unified deep learning anomaly detection and classification approach for smart grid environments. IEEE Trans. Netw. Serv. Manage.Netw. Serv. Manage. 18(2), 1137–1151 (2021)

    Article  Google Scholar 

  60. Al-Abassi, A., Karimipour, H., Dehghantanha, A., Parizi, R.M.: An ensemble deep learning-based cyber-attack detection in industrial control system. IEEE Access 8, 83965–83973 (2020)

    Article  Google Scholar 

  61. He, Y., Mendis, G.J., Wei, J.: Real-time detection of false data injection attacks in smart grid: A deep learning-based intelligent mechanism. IEEE Transactions on Smart Grid 8(5), 2505–2516 (2017)

    Article  Google Scholar 

  62. Wilson, D., Tang, Y., Yan, J., & Lu, Z. (2018, August). Deep learning-aided cyber-attack detection in power transmission systems. In 2018 IEEE Power & Energy Society General Meeting (PESGM) (pp. 1–5). IEEE.

    Google Scholar 

  63. Sengan, S., Subramaniyaswamy, V., Indragandhi, V., Velayutham, P., Ravi, L.: Detection of false data cyber-attacks for the assessment of security in smart grid using deep learning. Comput. Electr. Eng.. Electr. Eng. 93, 107211 (2021)

    Article  Google Scholar 

  64. Wang, H., Ruan, J., Wang, G., Zhou, B., Liu, Y., Fu, X., Peng, J.: Deep learning-based interval state estimation of AC smart grids against sparse cyber attacks. IEEE Trans. Industr. Inf.Industr. Inf. 14(11), 4766–4778 (2018)

    Article  Google Scholar 

  65. Ismail, M., Shaaban, M.F., Naidu, M., Serpedin, E.: Deep learning detection of electricity theft cyber-attacks in renewable distributed generation. IEEE Transactions on Smart Grid 11(4), 3428–3437 (2020)

    Article  Google Scholar 

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Authors and Affiliations

  1. Nalla Malla Reddy Engineering College, Hyderabad, Telangana, India

    Bhaskar Patnaik

  2. Department of Electrical and Electronics Engineering, Siksha O Anusandhan University, Bhubaneswar, India

    Manohar Mishra

  3. Department of Electrical and Electronics Engineering, Birla Institute of Technology & Science, Dubai Campus, Dubai, United Arab Emirates

    Shazia Hasan

Authors
  1. Bhaskar Patnaik
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  2. Manohar Mishra
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  3. Shazia Hasan
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Corresponding author

Correspondence to Manohar Mishra .

Editor information

Editors and Affiliations

  1. Department of Computer Science, MSCB University, Baripada, India

    Janmenjoy Nayak

  2. Department of Computer Application, Veer Surendra Sai University of Technology, Sambalpur, India

    Bighnaraj Naik

  3. Department of Artificial Intelligence and Data Science, Sri Eshwar College of Engineering, Coimbatore, Tamil Nadu, India

    Vimal S

  4. Institute of Informatics and Telecommunications, Reshetnev Siberian State University of Science and Technology, Krasnoyarsk, Russia

    Margarita Favorskaya

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Patnaik, B., Mishra, M., Hasan, S. (2024). Cyber-Physical Security in Smart Grids: A Holistic View with Machine Learning Integration. In: Nayak, J., Naik, B., S, V., Favorskaya, M. (eds) Machine Learning for Cyber Physical System: Advances and Challenges. Intelligent Systems Reference Library, vol 60. Springer, Cham. https://doi.org/10.1007/978-3-031-54038-7_12

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