Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Advertisement

Springer Nature Link
Log in

Detection of cyber attacks in smart grids using SVM-boosted machine learning models

  • Special Issue Paper
  • Published:
Service Oriented Computing and Applications Aims and scope Submit manuscript

Abstract

False data injection, sometimes known as FDI, is a common form of assault that is launched against smart grids. The faulty data detection methods that are now in use are unable to detect covert FDI attacks, making it impossible to do so (El Mrabet et al. 67:469–482, 2018). The detection of FDI assaults can be accomplished using a multitude of methods, one of which is machine learning. In this paper, six alternative supervised learning (SVM-FS) hybrid methods, each with its own set of six unique boosted and feature selection (FS) procedures, are analyzed. These strategies are based on SVM-boosting algorithmic frameworks. Using data obtained from the smart grid, an analysis of the adaptability of these approaches is carried out. The accuracy of each detection method in terms of its classification is what is used to evaluate them. When supervised learning and hybrid methods are utilized in a simulation exercise, the performance of the classification techniques that are used to detect FDI attacks is significantly improved.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23

Similar content being viewed by others

Data availability

As you are concerned with the availability of my code and practical work. It is confidential to my lab and cannot be shared with anyone until it gets published. And secondly, I will publish my code and lab work according to the instructions of my supervisor.

Notes

  1. www.kaggle.com.

References

  1. El Mrabet Z, Kaabouch N, El Ghazi H, El Ghazi H (2018) Cyber-security in smart grid: survey and challenges. Comput Electr Eng 67:469–482

  2. Rajendran G, Sathyabalu HV, Sachi M, Devarajan V (2019) Cyber security in smart grid: challenges and solutions. In: 2019 2nd International Conference on Power and Embedded Drive Control (ICPEDC), 5(2020): 546–551.https://doi.org/10.1109/ICPEDC47771.2019.9036484

  3. Qi R, Rasband C, Zheng J, Longoria R (2021) Detecting cyber attacks in smart grids using semi-supervised anomaly detection and deep representation learning. Information. https://doi.org/10.3390/info12080328

    Article  Google Scholar 

  4. Saghezchi FB, Mantas G, Violas MA, de Oliveira Duarte AM, Rodriguez J (2022) Machine Learning for DDoS Attack Detection in Industry 40 CPPSs. Electronics 11(4):1–14. https://doi.org/10.3390/electronics11040602

    Article  Google Scholar 

  5. Intruders L, Grids S, Rouzbahani HM, Karimipour, Lei L (2022) Multi-layer defense algorithm against deep reinforcement multi-layer defense algorithm against deep reinforcement learning-based intruders in smart grids. pp 0–10. doi: https://doi.org/10.36227/techrxiv.19398449.v1.

  6. Kumar A, Saxena N, Choi BJ (2021) Machine learning algorithm for detection of false data injection attack in power system. Int Conf Inf Netw 2021(Janua):385–390. https://doi.org/10.1109/ICOIN50884.2021.9333913

    Article  Google Scholar 

  7. Sakhnini J, Karimipour H, Dehghantanha A (2019) Smart grid cyber attacks detection using supervised learning and heuristic feature selection. In: 2019 IEEE 7th International Conference on Smart Energy Grid Engineering (SEGE), pp 108–112, doi: https://doi.org/10.1109/SEGE.2019.8859946.

  8. Sakhnini J (2020) Security of smart cyber-physical grids : a deep learning approach

  9. Sayghe A et al (2020) Survey of machine learning methods for detecting false data injection attacks in power systems. IET Smart Grid 3(5):581–595. https://doi.org/10.1049/iet-stg.2020.0015

    Article  Google Scholar 

  10. You S, Kuo C-CJ (2019) Defending against adversarial attacks in deep neural networks. Artif Intell Mach Learn Multi Domain Oper Appl. https://doi.org/10.1117/12.2519268

    Article  Google Scholar 

  11. Sundar K, Neyaz A, Liu Q (2021) IoT network attack detection using supervised machine learning. Int J Artif Intell Expert Syst 10(2):32

    Google Scholar 

  12. Yehia DM, Mansour DEA (2018) Modeling and analysis of superconducting fault current limiter for system integration of battery banks. IEEE Trans Appl Supercond 28(4):1–6. https://doi.org/10.1109/TASC.2018.2814398

    Article  Google Scholar 

  13. Mohanram S, Chandralekha R (2016) Analysis of SFCL’s in DC system with renewable energy sources. Int J Eng Technol 7(6):2084–2091

    Google Scholar 

  14. Wilson D, Tang Y, Yan J, Lu Z (2018) Deep learning-aided cyber-attack detection in power transmission systems. IEEE Power Energy Soc General Meet PESGM. https://doi.org/10.1109/PESGM.2018.8586334

    Article  Google Scholar 

  15. Ashrafuzzaman M, Das S, Chakhchoukh Y, Shiva S, Sheldon FT (2020) Detecting stealthy false data injection attacks in the smart grid using ensemble-based machine learning. Comput Secur 97:101994. https://doi.org/10.1016/j.cose.2020.101994

    Article  Google Scholar 

  16. Hussein AA, Ali MH (2014) Comparison among series compensators for fault ride through capability enhancement of wind generator systems. Int J Renew Energy Res 4(3):767–776

    Google Scholar 

  17. Ye L, Lin LZ (2010) Study of superconducting fault current limiters for system integration of wind farms. IEEE Trans Appl Supercond 20(3):1233–1237. https://doi.org/10.1109/TASC.2009.2039469

    Article  Google Scholar 

  18. Chehri A, Fofana I, Yang X (2021) Security risk modeling in smart grid critical infrastructures in the era of big data and artificial intelligence. Sustainability 13(6):1–19. https://doi.org/10.3390/su13063196

    Article  Google Scholar 

  19. Tarafdar Hagh M, Muttaqi KM, Sutanto D, Hossain MSA, Haidar AMA (2015) Improving fault ride-through capability of DFIG based wind generators by using bridge-type superconducting fault current limiter. Proc Univ Power Eng Conf. https://doi.org/10.1109/UPEC.2015.7339856

    Article  Google Scholar 

  20. Sengan S, Subramaniyaswamy V, Indragandhi V, Velayutham P, Ravi L (2021) Detection of false data cyber-attacks for the assessment of security in smart grid using deep learning. Comput Electr Eng 93:107211. https://doi.org/10.1016/j.compeleceng.2021.107211

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. College of Computer and Information Science, Jouf University, Sakakah, Saudi Arabia

    Hathal Salamah Alwageed

Authors
  1. Hathal Salamah Alwageed
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hathal Salamah Alwageed.

Ethics declarations

Conflicts of interest

There is no conflict of interest between authors.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Alwageed, H.S. Detection of cyber attacks in smart grids using SVM-boosted machine learning models. SOCA 16, 313–326 (2022). https://doi.org/10.1007/s11761-022-00349-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11761-022-00349-1

Keywords

Search

Navigation