Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

access icon free Deep learning based method for false data injection attack detection in AC smart islands

© The Institution of Engineering and Technology
Received 01/03/2020, Accepted 04/05/2020, Revised 23/04/2020, Published 19/05/2020

This paper investigates the false data injection attacks (FDIA) in an AC smart island and the detection solution of the attack on distributed energy resources in a smart island. In this study, a new scheme of FDIA detection is proposed based on wavelet singular values as input index of deep learning algorithm. In the proposed method, switching surface based on sliding mode control breaks down for adjusting accurate factors of wavelet transform and then features of wavelet coefficients are extracted by singular value decomposition. Indexes are determined according to the wavelet singular values in switching surface of voltage and current which defines the input indexes of deep machine learning and detecting FDIA. This cyber-protection plan has been put forward for cyber diagnostic and examined in different types of attacks happening in voltage and current signals derivation of measuring sensors as well as sending and receiving data from communication and control systems. The main priority of the suggested detection plan is the high capability to detect FDIA with a high accuracy. To show the effectiveness of the proposed method, simulation studies are performed on AC smart island in MATLAB/Simulink environment.

Inspec keywords: fault diagnosis; variable structure systems; security of data; smart power grids; power engineering computing; learning (artificial intelligence); power system security; power distribution faults; singular value decomposition; distributed power generation; wavelet transforms

Other keywords: distributed energy resources; detection solution; wavelet coefficients; suggested detection plan; input index; false data injection attack detection; deep machine learning; singular value decomposition; AC smart island; deep learning algorithm; false data injection attacks; wavelet singular values; FDIA detection

Subjects: Power engineering computing; Distribution networks; Other topics in statistics; Multivariable control systems; Distributed power generation; Data security; Knowledge engineering techniques

References

    1. 1)
      • 16. Luo, X., Wang, X., Pan, X., et al: ‘Detection and isolation of false data injection attack for smart grids via unknown input observers’, IET Gener. Transm. Distrib., 2019, 13, (8), pp. 1277–1286.
    2. 2)
      • 2. Khooban, M.-H., Dehghani, M., Dragičević, T.: ‘Hardware-in-the-loop simulation for the testing of smart control in grid-connected solar power generation systems’, Int. J. Comput. Appl. Technol., 2018, 58, (2), pp. 116–128.
    3. 3)
      • 9. Wang, Q., Tai, W., Tang, Y., et al: ‘Review of the false data injection attack against the cyber-physical power system’, IET Cyber-Phys. Syst.: Theory Appl., 2019, 4, (2), pp. 101–107.
    4. 4)
      • 20. Heaton, J.: ‘AIFH, volume 3: deep learning and neural networks’, 2015.
    5. 5)
      • 12. Alcaraz, C., Fernandez-Gago, C., Lopez, J.: ‘An early warning system based on reputation for energy control systems’, IEEE Trans. Smart Grid, 2011, 2, (4), pp. 827–834.
    6. 6)
      • 28. Hinton, G.E., Salakhutdinov, R.R.: ‘Reducing the dimensionality of data with neural networks’, Science, 2006, 313, (5786), pp. 504–507.
    7. 7)
      • 18. Moslemi, R., Velni, J.M., Mesbahi, A.: ‘A weighted graph-based method for detection of data integrity attacks in electricity markets’, IET Gener. Transm. Distrib., 2019, 13, pp. 1–9.
    8. 8)
      • 10. Khan, R., Mclaughlin, K., Laverty, D., et al: ‘Design and implementation of security gateway for synchrophasor based real-time control and monitoring in smart grid’, Ieee Access, 2017, 5, pp. 11626–11644.
    9. 9)
      • 23. Singh, S., Kumar, N.: ‘Detection of bearing faults in mechanical systems using stator current monitoring’, IEEE Trans. Ind. Inf., 2016, 13, (3), pp. 1341–1349.
    10. 10)
      • 26. Lei, Y., Jia, F., Zhou, X., et al: ‘A deep learning-based method for machinery health monitoring with big data’, J. Mech. Eng., 2015, 51, (21), pp. 49–56.
    11. 11)
      • 22. Liu, Y., Ning, P., Reiter, M.K.: ‘False data injection attacks against state estimation in electric power grids’, ACM Trans. Inf. Syst. Sec. (TISSEC), 2011, 14, (1), pp. 1–33.
    12. 12)
      • 8. Babineau, G.L., Jones, R.A., Horowitz, B.: ‘A system-aware cyber security method for shipboard control systems with a method described to evaluate cyber security solutions’. 2012 IEEE Conf. on Technologies for Homeland Security (HST), Waltham, MA, USA, 2012, pp. 99–104.
    13. 13)
      • 3. Dragičević, T., Lu, X., Vasquez, J.C., et al: ‘DC microgrids—part II: A review of power architectures, applications, and standardization issues’, IEEE Trans. Power Electron., 2015, 31, (5), pp. 3528–3549.
    14. 14)
      • 4. Ghiasi, M., Dehghani, M., Niknam, T., et al: ‘Investigating overall structure of cyber-attacks on smart-grid control systems to improve cyber resilience in power system’, IEEE Smart Grid Newsletter, 2020, 1, pp. 1–6.
    15. 15)
      • 7. Sun, C.-C., Hahn, A., Liu, C.-C.: ‘Cyber security of a power grid: state-of-the-art’, Int. J. Electr. Power Energy Syst., 2018, 99, pp. 45–56.
    16. 16)
      • 25. Dehghani, M., Khooban, M.H., Niknam, T.: ‘Fast fault detection and classification based on a combination of wavelet singular entropy theory and fuzzy logic in distribution lines in the presence of distributed generations’, Int. J. Electr. Power Energy Syst., 2016, 78, pp. 455–462.
    17. 17)
      • 21. Mulquin, M.J.: ‘Roles of IEC in supporting effective smart city standards’, IET Smart Cities, 2019, 1, (1), pp. 10–18.
    18. 18)
      • 17. Rahman, A., Mohsenian-Rad, H.: ‘False data injection attacks against nonlinear state estimation in smart power grids’. 2013 IEEE Power & Energy Society General Meeting, Vancouver, BC, Canada, 2013, pp. 1–5.
    19. 19)
      • 15. Liu, L., Esmalifalak, M., Ding, Q., et al: ‘Detecting false data injection attacks on power grid by sparse optimization’, IEEE Trans. Smart Grid, 2014, 5, (2), pp. 612–621.
    20. 20)
      • 24. Biswal, B., Vyshnavi, E., Metta, S., et al: ‘Robust retinal optic disc and optic cup segmentation via stationary wavelet transform and Maximum vessel pixel sum’, IET Image Process., 2019, 14, pp. 592–602.
    21. 21)
      • 19. Soltan, S., Yannakakis, M., Zussman, G.: ‘Power grid state estimation following a joint cyber and physical attack’, IEEE Trans. Control Netw. Syst., 2016, 5, (1), pp. 499–512.
    22. 22)
      • 11. Xu, R., Wang, R., Guan, Z., et al: ‘Achieving efficient detection against false data injection attacks in smart grid’, IEEE Access, 2017, 5, pp. 13787–13798.
    23. 23)
      • 6. Mashayekh, S., Butler-Purry, K.L.: ‘An integrated security-constrained model-based dynamic power management approach for isolated microgrids in all-electric ships’, IEEE Trans. Power Syst., 2015, 30, (6), pp. 2934–2945.
    24. 24)
      • 27. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ‘Imagenet classification with deep convolutional neural networks’. Advances in Neural Information Processing Systems, Lake Tahoe, Nevada, USA, 2012, pp. 1097–1105.
    25. 25)
      • 29. Chen, Y., Lin, Z., Zhao, X., et al: ‘Deep learning-based classification of hyperspectral data’, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 2014, 7, (6), pp. 2094–2107.
    26. 26)
      • 5. Zhu, X., Xie, Z., Jing, S., et al: ‘Distributed virtual inertia control and stability analysis of dc microgrid’, IET Gener. Transm. Distrib., 2018, 12, (14), pp. 3477–3486.
    27. 27)
      • 1. Dehghani, M., Khooban, M.H., Niknam, T., et al: ‘Time-varying sliding mode control strategy for multibus low-voltage microgrids with parallel connected renewable power sources in islanding mode’, J. Energy Eng., 2016, 142, (4), p. 05016002.
    28. 28)
      • 14. Abdollah, K.-F., Su, W., Jin, T.: ‘A machine learning based cyber attack detection model for wireless sensor networks in microgrids’, IEEE Trans. Ind. Inf., 2020, 16, pp. 1–8.
    29. 29)
      • 13. 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. Over Netw., 2017, 4, (1), pp. 70–81.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-gtd.2020.0391
Loading

Related content

content/journals/10.1049/iet-gtd.2020.0391
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
Print this page
This is a required field
Please enter a valid email address