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Fog computing-based IoT-enabled system security for electrical vehicles in the smart grid

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Abstract

Wireless sensor networks (WSNs) face significant challenges related to dependability, integrity, and confidentiality due to their widespread deployment. Intrusion detection emerges as a pivotal active defense technology to fortify the security of WSNs. Given the unique characteristics of WSNs, achieving a balance between precise data transmission and the limited energy resources of sensors, as well as between effective detection and optimal utilization of network resources, becomes imperative. In this research, we propose a fog computing-based intrusion detection system (IDS) tailored for a smart power grid environment. The primary focus of this article is to elucidate the application of IDS within the context of a smart grid. We present a novel stacked model based on ensemble learning algorithms, adept at accurately delineating the interconnections among fog nodes susceptible to cyber-attacks. To validate the efficacy of our approach, we conduct a series of comparative experiments utilizing the KDD-Cup-99 dataset with cross-validation. Our findings reveal that the Fog-IDS and Stacking-based approaches significantly enhance the performance of the IDS. Through simulations, we substantiate that our proposed approach effectively analyzes fog nodes using the deployed IDS, enabling the prediction of energy requirements at refill stations. Achieving an impressive accuracy rate exceeding 99.837%, MCC rate of 99.73%, and F1-score rate of 99.827%, our study underscores the robustness and reliability of the Fog-IDS and Stacking-based methods in augmenting IDS performance in WSNs.

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References

  1. Kabalci Y, Kabalci E, Padmanaban S, Holm-Nielsen JB, Blaabjerg F (2019) Internet of things applications as energy internet in smart grids and smart environments. Electronics 8(9):972

    Article  Google Scholar 

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

    Article  Google Scholar 

  3. Ekanayake JB, Jenkins N, Liyanage K, Wu J, Yokoyama A (2012) Smart grid: technology and applications. Wiley

    Book  Google Scholar 

  4. Abir SAA, Anwar A, Choi J, Kayes A (2021) Iot-enabled smart energy grid: applications and challenges. IEEE access 9:50961–50981

    Article  Google Scholar 

  5. Peng K, Leung V, Zheng L, Wang S, Huang C, Lin T et al (2018) Intrusion detection system based on decision tree over big data in fog environment. Wirel Commun Mob Comput 2018:4680867

    Article  Google Scholar 

  6. Fakhar A, Haidar AM, Abdullah M, Das N (2023) Smart grid mechanism for green energy management: a comprehensive review. Int J Green Energy 20(3):284–308

    Article  Google Scholar 

  7. Karar A, Das N, Tan CL, Alameh K, Lee YT (2010) Design of high-sensitivity plasmonics-assisted gaas metal-semiconductor-metal photodetectors. In: 7th international symposium on high-capacity optical networks and enabling technologies, IEEE, pp 138–142

  8. Masouleh FF, Das N, Mashayekhi HR (2014) Optimization of light transmission efficiency for nano-grating assisted MSM-PDs by varying physical parameters. Photonics Nanostruct Fundam Appl 12(1):45–53

    Article  Google Scholar 

  9. Patel K, Das N, Khan MMK (219) Optimization of hybrid solar, wind and diesel energy systems from economic point of view. In: 2019 29th Australasian Universities power engineering conference (AUPEC), IEEE, pp 1–6

  10. Das N, Tan CL, Lysak VV, Alameh K, Lee YT (2009) Light absorption enhancement in metal-semiconductor-metal photodetectors using plasmonic nanostruc ture gratings. In: 2009 6th international symposium on high capacity optical networks and enabling technologies (HONET), IEEE, pp 86–90

  11. Arefin SS, Das N (2017) Optimized hybrid wind-diesel energy system with feasibility analysis. Technol Econ Smart Grids Sustain Energy 2:1–8

    Article  Google Scholar 

  12. Labiod Y, Amara Korba A, Ghoualmi N (2022) Fog computing-based intrusion detection architecture to protect IoT networks. Wirel Pers Commun 125(1):231–259

    Article  Google Scholar 

  13. Vimal S et al (2020) Edge computing-based intrusion detection system for smart cities development using IoT in urban areas. Internet of things in smart technologies for sustainable urban development, pp 219–237

  14. Merad-Boudia OR, Senouci SM (2020) An efficient and secure multidimensional 23data aggregation for fog-computing-based smart grid. IEEE Internet Things J 8(8):6143–6153

    Article  Google Scholar 

  15. Ferrag MA, Babaghayou M, Yazici MA (2020) Cyber security for fog-based smart grid scada systems: solutions and challenges. J Inf Secur Appl 52:102500

    Google Scholar 

  16. Afolabi HA, Aburas A (2021) Proposed back propagation deep neural network for intrusion detection in Internet of Things fog computing. Int J 9(4):464–469

    Google Scholar 

  17. Lin S-Y, Du Y, Ko P-C, Wu T-J, Ho P-T, Sivakumar V et al (2020) Fog computing based hybrid deep learning framework in effective inspection system for smart manufacturing. Comput Commun 160:636–642

    Article  Google Scholar 

  18. Diro AA, Chilamkurti N, Kumar N (2017) Lightweight cybersecurity schemes using elliptic curve cryptography in publish-subscribe fog computing. Mob Netw Appl 22:848–858

    Article  Google Scholar 

  19. Sadaf K, Sultana J (2020) Intrusion detection based on autoencoder and isolation forest in fog computing. IEEE Access 8:167059–167068

    Article  Google Scholar 

  20. Prabavathy S, Sundarakantham K, Shalinie SM (2018) Design of cognitive fog computing for intrusion detection in internet of things. J Commun Netw 20(3):291–298

    Article  Google Scholar 

  21. Alghamdi F, Mahfoudh S, Barnawi A (2019) A novel fog computing based architecture to improve the performance in content delivery networks. Wirel Commun Mob Comput 2019:1–13

    Article  Google Scholar 

  22. Ahmad R, Alsmadi I (2021) Machine learning approaches to IoT security: a systematic literature review. Internet Things 14:100365

    Article  Google Scholar 

  23. Diaba SY, Elmusrati M (2023) Proposed algorithm for smart grid DDoS detection based on deep learning. Neural Netw 159:175–184

    Article  Google Scholar 

  24. Chicco D, Jurman G (2023) The matthews correlation coefficient (MCC) should replace the ROC AUC as the standard metric for assessing binary classification. BioData Min 16(1):1–23

    Article  Google Scholar 

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Author notes

  1. Vibha Kaw Raina, Bharat Bhushan Sagar and Ramesh C. Bansal have contributed equally to this work.

Authors and Affiliations

  1. CSE, Birla Institute of Technology, Mesra, Ranchi, Jharkhand, 835215, India

    Sanjay Kumar Sonker & Vibha Kaw Raina

  2. CSE, Harcourt Butler Technical University, Kanpur, Uttar Pradesh, 208002, India

    Bharat Bhushan Sagar

  3. Department of Electrical Engineering, University of Sharjah, Sharjah, United Arab Emirates

    Ramesh C. Bansal

  4. Department of Electric, Electronic and Computer Engineering, University of Pretoria, Pretoria, South Africa

    Ramesh C. Bansal

Authors
  1. Sanjay Kumar Sonker
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  2. Vibha Kaw Raina
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  3. Bharat Bhushan Sagar
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  4. Ramesh C. Bansal
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Proposed model for fog-based IDS system

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Correspondence to Sanjay Kumar Sonker.

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Sonker, S.K., Raina, V.K., Sagar, B.B. et al. Fog computing-based IoT-enabled system security for electrical vehicles in the smart grid. Electr Eng 106, 1339–1355 (2024). https://doi.org/10.1007/s00202-024-02256-4

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  • DOI: https://doi.org/10.1007/s00202-024-02256-4

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