Skip to main content

A Novel Deception Defense-Based Honeypot System for Power Grid Network

  • Conference paper
  • First Online:
Smart Computing and Communication (SmartCom 2021)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13202))

Included in the following conference series:

  • 1144 Accesses

Abstract

In recent years, as cyber-attacks have become more and more rampant, power grid networks are also facing more and more security threats, which have gradually become the focus attention of attackers. Traditional defense methods are represented by intrusion detection systems and firewalls, whose main purpose is to keep attackers out. However, with the diversification, concealment and complexity of attack methods, traditional defense methods are usually difficult to cope with the endless attack methods. To this end, this paper proposes a new type of honeypot system based on deception defense technology. While retaining the nature of the honeypot, it adopts dynamic deception approach to actively collect unused IP addresses in the power grid networks. Then, these unused IP addresses are used to construct dynamic virtual hosts. When an attacker initiates network access to these dynamic virtual hosts, they will proactively respond to the attacker or redirect the attack traffic to the honeypot in the background, thereby deceiving and trapping the attacker. The experimental results show that the proposed honeypot system can effectively expands the monitoring range of traditional honeypots and has a good defense effect against unknown attacks, thus effectively making up for the shortcomings of traditional defense methods.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 89.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Qiu, M.: Low-power low-latency data allocation for hybrid scratch-pad memory. IEEE Embed. Syst. Lett. 6(4), 69–72

    Google Scholar 

  2. Qiu, M., et al.: RNA nanotechnology for computer design and in vivo computation. Philos. Trans. R. Soc. A (2013)

    Google Scholar 

  3. Guo, Y., et al.: Optimal data allocation for scratch-pad memory on embedded multi-core systems. In: IEEE ICPP Conference, pp. 464–471 (2011)

    Google Scholar 

  4. Zhang, L., Qiu, M., Tseng, W., Sha, E.: Variable partitioning and scheduling for MPSoC with virtually shared scratch pad memory. J. Signal Process. Syst. 58(2), 247–265 (2010)

    Article  Google Scholar 

  5. Lu, R., Jin, X., Zhang, S., Qiu, M., Wu, X.: A study on big knowledge and its engineering issues. IEEE Trans. Knowl. Data Eng. 31(9), 1630–1644 (2018)

    Article  Google Scholar 

  6. Guo, Q., et al.: A remote test method for power grid security and stability control system and its engineering application. In: E3S Web of Conference. EDP Science, p. 260 (2021)

    Google Scholar 

  7. Eskandarpour, R., Gokhale, P., Khodaei, A., et al.: Quantum computing for enhancing grid security. IEEE Trans. Power Syst. 35(5), 4135–4137 (2020)

    Article  Google Scholar 

  8. Aoufi, S., et al.: Survey of false data injection in smart power grid: attacks, countermeasures and challenges. J. Inform. Secur. Appl. 54, 102518 (2020)

    Google Scholar 

  9. Butt, O.M., Zulqarnain, M., Butt, T.M.: Recent advancement in smart grid technology: future prospects in the electrical power network. Ain Shams Eng. J. 12(1), 687–695 (2021)

    Article  Google Scholar 

  10. Gunduz, M.Z., Das, R.: Cyber-security on smart grid: threats and potential solutions. Comput. Netw. 169, 107094 (2020)

    Google Scholar 

  11. Bou-Harb, E., Debbabi, M., Assi, C.: Cyber scanning: a comprehensive survey. IEEE Commun. Surv. Tutor. 16(3), 1496–1519 (2013)

    Article  Google Scholar 

  12. Cho, J.-H., et al.: Toward proactive, adaptive defense: a survey on moving target defense. IEEE Commun. Surv. Tutor. 22(10), 709–745 (2020)

    Google Scholar 

  13. Thakur, K., Qiu, M., Gai, K., Ali, M.: An investigation on cyber security threats and security models. In: IEEE CSCloud (2015)

    Google Scholar 

  14. Gai, K., Qiu, M., Sun, X., Zhao, H.: Security and privacy issues: a survey on FinTech. In: SmartCom, pp. 236–247 (2016)

    Google Scholar 

  15. Zhang, Z., et al.: Jamming ACK attack to wireless networks and a mitigation approach. In: IEEE GLOBECOM Conference, pp. 1–5 (2008)

    Google Scholar 

  16. Qiu, M., et al.: A novel energy-aware fault tolerance mechanism for wireless sensor networks. In: IEEE/ACM Conference on Green Computing and Communications (2011)

    Google Scholar 

  17. Antonatos, S., Akritidis, P., Markatos, E.P., et al.: Defending against hitlist worms using network address space randomization. Comput. Netw. 51(12), 3471–3490 (2007)

    Article  Google Scholar 

  18. Jafarian, J.H., Al-Shaer, E., Duan, Q.: An effective address mutation approach for disrupting reconnaissance attacks. IEEE TIFS 10(12), 2562–2577 (2015)

    Google Scholar 

  19. Jajodia, S., et al.: Cyber Deception. Springer, Heidelberg (2016)

    Google Scholar 

  20. Qiu, H., Qiu, M., Lu, Z.: Selective encryption on ECG data in body sensor network based on supervised machine learning. Inf. Fusion 55, 59–67 (2020)

    Article  Google Scholar 

  21. Qiu, H., Qiu, M., Memmi, G., Ming, Z., Liu, M.: A dynamic scalable blockchain based communication architecture for IoT. In: SmartBlock, 159–166 (2018)

    Google Scholar 

  22. Naik, N., Jenkins, P., Savage, N., et al.: A computational intelligence enabled honeypot for chasing ghosts in the wires. Complex Intell. Syst. 7(1), 477–494 (2021)

    Article  Google Scholar 

  23. Sun, Y., Tian, Z., Li, M., et al.: Honeypot identification in softwarized industrial cyber-physical systems. IEEE Trans. Ind. Inf. 17(8), 5542–5551 (2020)

    Article  Google Scholar 

  24. Mondal, A., Goswami, T.: Enhanced Honeypot cryptographic scheme and privacy preservation for an effective prediction in cloud security. Microprocess. Microsyst. 81, 103719 (2021)

    Google Scholar 

  25. Ziaie Tabari, A., Ou, X.: A multi-phased multi-faceted iot honeypot ecosystem. In: ACM SIGSAC Conference on Computer and Communications Security, pp. 2121–2123 (2020)

    Google Scholar 

  26. Naik, N., et al.: D-FRI-Honeypot: a secure sting operation for hacking the hackers using dynamic fuzzy rule interpolation. IEEE Trans. Emerg. Top. Comput. Intell. (2020)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Feng, M. et al. (2022). A Novel Deception Defense-Based Honeypot System for Power Grid Network. In: Qiu, M., Gai, K., Qiu, H. (eds) Smart Computing and Communication. SmartCom 2021. Lecture Notes in Computer Science, vol 13202. Springer, Cham. https://doi.org/10.1007/978-3-030-97774-0_27

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-97774-0_27

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-97773-3

  • Online ISBN: 978-3-030-97774-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics