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Detection of interruption attack in the wireless networked closed loop industrial control systems

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Abstract

Due to tremendous growth in the network, security is a major concern and it is gaining a high level of attention in the system. In the Industrial control systems such as SCADA wind turbines, intruders target to corrupt information by making traffic. A Novel method known as Mesh Intruder detachment scheme has proposed to overcome such influential attacks. Enhanced intrusion detection based traffic optimization and deep learning based neural network algorithm are used to detect delay and passage caused by the intruders. The proposed scheme acts in two phases. In the first phase, redundant information such as unwanted distortions, frequency specification changes was removed and selected features were extracted for optimization. In the second phase, an Interruption attack has been detected and labeled which causes damage to the growth of production.

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Abbreviations

EID-TO:

Enhanced intrusion detection based traffic optimization

DL-NN:

Deep learning based neural network algorithm

SCADA:

Supervisory control and data acquisition

IDS:

Intrusion detection system

FDR:

Flight data recorder

PPV:

Prediction of peak velocity

FDR:

False detection rate

NPV:

Net present value

STO:

Surplus training optimization

GA:

Graphical artificial intelligence

TP:

True positive

TN:

True negative

FP:

False positive

FP:

False negative

References

  1. Wei, H., Chen, H., Guo, Y., Jing, G., & Tao, J. (2016). A survey of network anomaly detection techniques. Journal of Network and Computer Applications,60, 19–31.

    Article  Google Scholar 

  2. Yang, Y., McLaughlin, K., Sezer, S., Littler, T., Im, E. G., Pranggono, B., et al. (2016). A concise model to evaluate the security of SCADA systems based on security standards. International Journal of Computer Applications,111(14), 1–9.

    Google Scholar 

  3. Sezer, S., Pranggono, B., & Wang, H. F. (2017). Host-based intrusion detection systems adapted from agent-based artificial immune systems. Neurocomputing Journal,88, 78–86.

    Google Scholar 

  4. Suthaharan, S., Alzahrani, M., Rajasegarar, S., Leckie, C., & Palaniswami, M. (2016). Building an intrusion detection system using a filter-based feature selection algorithm. IEEE Transactions on Computers,99, 1–13.

    Google Scholar 

  5. Sanyal, S., Das, N., & Sarkar, T. A. (2016). Accurate modeling of Modbus/TCP for intrusion detection in SCADA systems. International Journal of Critical Infrastructure Protection,6(2), 63–75.

    Google Scholar 

  6. Das, N., & Sarkar, T. (2016). Intrusion detection system: A comprehensive review. Journal of Network and Computer Applications,36(1), 16–24.

    Google Scholar 

  7. Shahzad, A., Xiong, N. N., Irfan, M., Lee, M., Hussain, S., & Khaltar, B. (2015). A SCADA intermediate simulation platform to enhance the system security. In 2015 17th International conference on advanced communication technology (ICACT) (pp. 368–373). IEEE.

  8. Liu, A. X., & Torng, E. (2016). Overlay automata and algorithms for fast and scalable regular expression matching. IEEE/ACM Transactions on Networking, 24(4), 2400–2415.

    Article  Google Scholar 

  9. Qiu, Y., Feny, Y., Sun, J., & Infield, D. (2016). Applying thermophysics for wind turbine drivetrain fault diagnosis using SCADA data. Institute of Engineering and Technology Journal,10(5), 661–668.

    Google Scholar 

  10. Mazidi, P., Du, M., Tjernberg, L. B., & Bobi, M. A. S. (2016). A performance and maintenance evaluation framework for wind turbines. International Journal on Probabilistic Methods Applied to Power Systems. https://doi.org/10.1109/pmaps.2016.7763931.

    Article  Google Scholar 

  11. Tautz-Weinert, J., & Watson, S. J. (2017). Using SCADA data for wind turbine condition monitoring—A review. Institution of Engineering and Technology Journal,11, 382–394.

    Google Scholar 

  12. Straczkiewicz, M., & Barszcz, T. (2016). Application of artificial neural network for damage detection in the planetary gearbox of wind turbine. Stock and Vibration Journal. https://doi.org/10.1155/2016/4086324.

    Article  Google Scholar 

  13. Marugan, A. P., & Marquez, F. P. G. (2017). SCADA and artificial neural networks for maintenance management. In International conference on management science and engineering management (pp. 912–919). Springer. https://doi.org/10.1007/978-3-319-59280-0_75

    Google Scholar 

  14. Fahad, A., & Mouftah, H. T. (2016). A network intrusion detection system based on a hidden Naïve Bayes multiclass classifier. Expert Systems with Applications,39(18), 13492–13500.

    Google Scholar 

  15. Almalawi, A., Yu, X., Tari, Z., Fahad, A., & Khalil, I. (2014). An unsupervised anomaly-based detection approach for integrity attacks on SCADA systems. Computers and Security,46, 94–110.

    Article  Google Scholar 

  16. Sayegh, N., Elhajj, I. H., Kayssi, A. I., & Chehab, A. (2014). SCADA intrusion detection system based on the temporal behavior of frequent patterns. In MELECON 2014—17th IEEE mediterranean electrotechnical conference (pp. 432–448). IEEE.

  17. Samdarshi, R., Sinha, N., & Tripathi, P. (2015). A triple layer intrusion detection system for SCADA security of electric utility. In 2015 Annual IEEE India conference (INDICON) (pp. 1–5).

  18. Almalawi, A., Fahad, A., Tari, Z., Alamri, A., AlGhamdi, R., & Zomaya, A.Y. (2016). An efficient data-driven clustering technique to detect attacks in SCADA systems. IEEE Transactions on Information Forensics and Security,11, 893–906.

    Article  Google Scholar 

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Acknowledgment

This research work has been confidentially acknowledged by Anna University recognized research center lab at V V College of Engineering, Tisaiyanvilai, India.

Funding

The data needed for the research work is taken from Gamesa Wind turbines. Since in Wind Turbines traffic based attacks and time delay based interruption attacks cause huge damage which leads to the growth in production, Real-time data stored in SCADA are taken and analyzed.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, V V College of Engineering, Tisaiyanvilai, India

    R. B. Benisha & S. Raja Ratna

Authors
  1. R. B. Benisha
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  2. S. Raja Ratna
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Correspondence to R. B. Benisha.

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Competing interests

R B. Benisha and Dr. S. Raja Ratna declare that they have no Competing interests.

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This article does not contain any studies with animal subjects performed by any of the authors.

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Mesh Intruder detachment scheme workflow in SCADA

Mesh Intruder detachment scheme workflow in SCADA

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Benisha, R.B., Raja Ratna, S. Detection of interruption attack in the wireless networked closed loop industrial control systems. Telecommun Syst 73, 359–370 (2020). https://doi.org/10.1007/s11235-019-00614-3

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  • DOI: https://doi.org/10.1007/s11235-019-00614-3

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