World Scientific
  • Search
    Quick Search in Journals
    Access type:
    Quick Search anywhere
    Access type:
    Advanced Search
  • My Cart
  •   
Skip main navigation
Close Drawer MenuOpen Drawer Menu

Cookies Notification

We use cookies on this site to enhance your user experience. By continuing to browse the site, you consent to the use of our cookies. Learn More
×

Our site uses Javascript to enchance its usability.You can disable your ad blocker or whitelist our website www.worldscientific.com to view the full content.

Select your blocker:

Adblock Plus Instructions

  1. Click the AdBlock Plus icon in the extension bar
  2. Click the blue power button
  3. Click refresh
Our website is made possible by displaying certain online content using javascript.
In order to view the full content, please disable your ad blocker or whitelist our website www.worldscientific.com.

System Upgrade on Tue, Oct 25th, 2022 at 2am (EDT)

Existing users will be able to log into the site and access content. However, E-commerce and registration of new users may not be available for up to 12 hours.
For online purchase, please visit us again. Contact us at [email protected] for any enquiries.
No Access

An Improved PMU Data Manipulation Attack Model

    https://doi.org/10.1142/S021812662250089XCited by:0 (Source: Crossref)
    Previous
    Next

    Abstract

    The importance of Phasor Manipulation Unit (PMU) in the smart grid makes it a target for attackers who can create PMU Data Manipulation Attacks (PDMA) by adding a small constant to change the magnitude and angle of the voltage and current captured by the PMU. To prevent the attack result from being detected by PDMA detection based on the properties of equivalent impedance, this paper proposes a collaborative step attack. In this attack, the equivalent impedance’s value on the end of the transmission line is equal whether before or after been attack, which is taken as the constraint condition. The objective function of it is to minimize the number of the elements which is not 0 in attack vector but this number is not 0. Turn a vector construction problem into an optimization problem by building objective functions and constraints and then we use the Alternating Direction Method of Multipliers (ADMM) and Convex Relaxation (CR) to solve. The experiment verifies the feasibility of using the CR-ADMM algorithm to construct attack vectors from two aspects of attack vector construction time and vector sparsity. Further, it uses the constructed attack vectors to carry out attacks on PMU. The experimental results show that the measurement value of PMU will change after the attack, but the equivalent impedance value at both ends of the transmission line remains the same. The attack vector successfully bypasses the PDMA detection method based on the property of equivalent impedance and the attack model constructed based on this method was more covert than the original model.

    This paper was recommended by Regional Editor Tongquan Wei.

    References

    • 1. J. Wang et al., Distributed framework for detecting PMU data manipulation attacks with deep autoencoders, J. IEEE Trans. Smart Grid 10 (2018) 4401–4410. Crossref, Web of Science, Google Scholar
    • 2. D. Wang et al., Extended distributed state estimation: A detection method against tolerable false data injection attacks in smart grids, J. Energies 7 (2014) 1517–1538. Crossref, Web of Science, Google Scholar
    • 3. M. Liao et al., An alternating direction method of multipliers based approach for PMU data recovery, J. IEEE Trans. Smart Grid 10 (2019) 4554–4565. Crossref, Web of Science, Google Scholar
    • 4. K. C. Sou, H. Sandberg and K. H. Johansson, On the exact solution to a smart grid cyber-security analysis problem, J. IEEE Trans. Smart Grid 4 (2011). Web of Science, Google Scholar
    • 5. K. C. Sou, H. Sandberg and K. H. Johansson, Computing critical k-tuples in power networks, J. IEEE Trans. Power Sys. 27 (2012) 1511–1520. Crossref, Web of Science, Google Scholar
    • 6. J. M. Hendrickx et al., Efficient Computations of a security index for false data attacks in power networks, J. IEEE Trans. Automat. Contr. 59 (2014) 3194–3208. Crossref, Web of Science, Google Scholar
    • 7. J. Hao et al., Sparse malicious false data injection attacks and defense mechanisms in smart grids, J. IEEE Trans. Industr. Inform. 11 (2017) 1–12. Web of Science, Google Scholar
    • 8. Y. Li and Y. Wang, State summation for detecting false data attack on smart grid, J. Int. J. Electric Power Energy Sys. 57 (2014) 156–163. Crossref, Web of Science, Google Scholar
    • 9. X. Wang et al., Online identification and data recovery for PMU data manipulation attack, J. IEEE Trans. Smart Grid 10 (2019) 5889–5898. Crossref, Web of Science, Google Scholar
    • 10. S. Pal and B. Sikdar, A mechanism for detecting data manipulation attacks on PMU data, in Proc. 2014 IEEE Inter. Conf. Communication Systems (ICCS) (Macau, China, 2014), pp. 19–21. Crossref, Google Scholar
    • 11. S. Pal, B. Sikdar and J. Chow, Classification and detection of PMU data manipulation attacks using transmission line parameters, J. IEEE Trans. Smart Grid 9 (2018) 5057–5066. Crossref, Web of Science, Google Scholar
    • 12. J. Zhou et al., Dependable scheduling for real-time workflows on cyber-physical cloud systems, IEEE Trans. Industr. Inform. 99 (2020) 1–1. Google Scholar
    • 13. S. Pal, Detection of gray hole attacks in synchrophasor network, master’s thesis, Rensselaer Polytechnic Institute, Troy, NY (2013). Google Scholar
    • 14. S. Pal, B. Sikdar and J. H. Chow, An online mechanism for detection of gray-hole attacks on PMU data, J. IEEE Trans. Smart Grid 9 (2018) 2498–2507. Crossref, Web of Science, Google Scholar
    • 15. X. Jiang et al., Spoofing GPS receiver clock offset of phasor measurement units, J. IEEE Trans. Power Sys. 28 (2013) 3253–3262. Crossref, Web of Science, Google Scholar
    • 16. Z. Zhang et al., Time synchronization attack in smart grid: Impact and analysis, J. IEEE Trans. Smart Grid 4 (2013) 87–98. Crossref, Web of Science, Google Scholar
    • 17. Y. Mohammad and A. Mahdi, Detection of PMU spoofing in power grid based on phasor measurement analysis, IET Gener. Transm. Distrib. 12 (2018) 1980–1987. Crossref, Web of Science, Google Scholar
    • 18. X. Fan, L. Du and D. Duan, Synchrophasor data correction under GPS spoofing attack: A state estimation based approach, IEEE Transactions on Smart Grid 9(5) (2017) 4538–4546. Crossref, Web of Science, Google Scholar
    • 19. P. Risbud, N. Gatsis and A. F. Taha, Vulnerability analysis of smart grids to GPS spoofing, IEEE Transactions on Smart Grid 10(4) (2018) 3535–3548. Crossref, Web of Science, Google Scholar
    • 20. G. Uganya, Radhika and N. Vijayaraj, A survey on internet of things: applications, recent issues, attacks, and security mechanisms, J. Circuits Syst. Comp. (2020) 1–1. Web of Science, Google Scholar