IDEAS home Printed from https://ideas.repec.org/a/gam/jftint/v15y2023i2p83-d1073360.html
   My bibliography  Save this article

Analysis of Cyber Security Attacks and Its Solutions for the Smart grid Using Machine Learning and Blockchain Methods

Author

Listed:
  • Tehseen Mazhar

    (Department of Computer Science, Virtual University of Pakistan, Lahore 51000, Pakistan)

  • Hafiz Muhammad Irfan

    (Department of Computer Science, Islamia University Bahawalpur, Bahawalnagar 62300, Pakistan)

  • Sunawar Khan

    (Department of Computer Science, Islamia University Bahawalpur, Bahawalnagar 62300, Pakistan)

  • Inayatul Haq

    (School of Information Engineering, Zhengzhou University, Zhengzhou 450001, China)

  • Inam Ullah

    (BK21 Chungbuk Information Technology Education and Research Center, Chungbuk National University, Cheongju 28644, Republic of Korea)

  • Muhammad Iqbal

    (Institute of Computing and Information Technology, Gomal University, Dera Ismail Khan 29220, Pakistan)

  • Habib Hamam

    (Faculty of Engineering, Université de Moncton, Moncton, NB E1A3E9, Canada
    Spectrum of Knowledge Production & Skills Development, Sfax 3027, Tunisia
    International Institute of Technology and Management, Commune d’Akanda, Libreville 1989, Gabon
    Department of Electrical and Electronic Engineering Science, School of Electrical Engineering, University of Johannesburg, Johannesburg 2006, South Africa)

Abstract

Smart grids are rapidly replacing conventional networks on a worldwide scale. A smart grid has drawbacks, just like any other novel technology. A smart grid cyberattack is one of the most challenging things to stop. The biggest problem is caused by millions of sensors constantly sending and receiving data packets over the network. Cyberattacks can compromise the smart grid’s dependability, availability, and privacy. Users, the communication network of smart devices and sensors, and network administrators are the three layers of an innovative grid network vulnerable to cyberattacks. In this study, we look at the many risks and flaws that can affect the safety of critical, innovative grid network components. Then, to protect against these dangers, we offer security solutions using different methods. We also provide recommendations for reducing the chance that these three categories of cyberattacks may occur.

Suggested Citation

  • Tehseen Mazhar & Hafiz Muhammad Irfan & Sunawar Khan & Inayatul Haq & Inam Ullah & Muhammad Iqbal & Habib Hamam, 2023. "Analysis of Cyber Security Attacks and Its Solutions for the Smart grid Using Machine Learning and Blockchain Methods," Future Internet, MDPI, vol. 15(2), pages 1-37, February.
    • Handle: RePEc:gam:jftint:v:15:y:2023:i:2:p:83-:d:1073360
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1999-5903/15/2/83/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1999-5903/15/2/83/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jianguo Ding & Attia Qammar & Zhimin Zhang & Ahmad Karim & Huansheng Ning, 2022. "Cyber Threats to Smart Grids: Review, Taxonomy, Potential Solutions, and Future Directions," Energies, MDPI, vol. 15(18), pages 1-37, September.
    2. Corey Dunn & Nour Moustafa & Benjamin Turnbull, 2020. "Robustness Evaluations of Sustainable Machine Learning Models against Data Poisoning Attacks in the Internet of Things," Sustainability, MDPI, vol. 12(16), pages 1-17, August.
    3. Han, Dong & Zhang, Chengzhenghao & Ping, Jian & Yan, Zheng, 2020. "Smart contract architecture for decentralized energy trading and management based on blockchains," Energy, Elsevier, vol. 199(C).
    4. Vidya Krishnan Mololoth & Saguna Saguna & Christer Åhlund, 2023. "Blockchain and Machine Learning for Future Smart Grids: A Review," Energies, MDPI, vol. 16(1), pages 1-39, January.
    5. Berghout, Tarek & Benbouzid, Mohamed & Muyeen, S.M., 2022. "Machine learning for cybersecurity in smart grids: A comprehensive review-based study on methods, solutions, and prospects," International Journal of Critical Infrastructure Protection, Elsevier, vol. 38(C).
    6. Md. Nazmul Hasan & Rafia Nishat Toma & Abdullah-Al Nahid & M M Manjurul Islam & Jong-Myon Kim, 2019. "Electricity Theft Detection in Smart Grid Systems: A CNN-LSTM Based Approach," Energies, MDPI, vol. 12(17), pages 1-18, August.
    7. Ke Zhang & Zhi Hu & Yufei Zhan & Xiaofen Wang & Keyi Guo, 2020. "A Smart Grid AMI Intrusion Detection Strategy Based on Extreme Learning Machine," Energies, MDPI, vol. 13(18), pages 1-19, September.
    8. Jiaxi Luo, 2022. "A Bibliometric Review on Artificial Intelligence for Smart Buildings," Sustainability, MDPI, vol. 14(16), pages 1-22, August.
    9. Justin Ugwu & Kenneth C. Odo & Chibuike Peter Ohanu & Jorge García & Ramy Georgious, 2022. "Comprehensive Review of Renewable Energy Communication Modeling for Smart Systems," Energies, MDPI, vol. 16(1), pages 1-28, December.
    10. He, Li & Liu, Yuanzhi & Zhang, Jie, 2021. "Peer-to-peer energy sharing with battery storage: Energy pawn in the smart grid," Applied Energy, Elsevier, vol. 297(C).
    11. Bhargav Appasani & Sunil Kumar Mishra & Amitkumar V. Jha & Santosh Kumar Mishra & Florentina Magda Enescu & Ioan Sorin Sorlei & Fernando Georgel Bîrleanu & Noureddine Takorabet & Phatiphat Thounthong , 2022. "Blockchain-Enabled Smart Grid Applications: Architecture, Challenges, and Solutions," Sustainability, MDPI, vol. 14(14), pages 1-33, July.
    12. Dongmin Yu & Zimeng Ma & Rijun Wang & Wen-Tsao Pan, 2022. "Efficient Smart Grid Load Balancing via Fog and Cloud Computing," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-11, May.
    13. Khazaei, Javad & Amini, M. Hadi, 2021. "Protection of large-scale smart grids against false data injection cyberattacks leading to blackouts," International Journal of Critical Infrastructure Protection, Elsevier, vol. 35(C).
    14. Ines Ortega-Fernandez & Francesco Liberati, 2023. "A Review of Denial of Service Attack and Mitigation in the Smart Grid Using Reinforcement Learning," Energies, MDPI, vol. 16(2), pages 1-15, January.
    15. Fan, Siyuan & Wang, Xiao & Cao, Shengxian & Wang, Yu & Zhang, Yanhui & Liu, Bingzheng, 2022. "A novel model to determine the relationship between dust concentration and energy conversion efficiency of photovoltaic (PV) panels," Energy, Elsevier, vol. 252(C).
    16. Ahl, Amanda & Yarime, Masaru & Tanaka, Kenji & Sagawa, Daishi, 2019. "Review of blockchain-based distributed energy: Implications for institutional development," Renewable and Sustainable Energy Reviews, Elsevier, vol. 107(C), pages 200-211.
    17. Seong-Kyu Kim & Jun-Ho Huh, 2018. "A Study on the Improvement of Smart Grid Security Performance and Blockchain Smart Grid Perspective," Energies, MDPI, vol. 11(8), pages 1-22, July.
    18. Wang, Wu & Harrou, Fouzi & Bouyeddou, Benamar & Senouci, Sidi-Mohammed & Sun, Ying, 2022. "Cyber-attacks detection in industrial systems using artificial intelligence-driven methods," International Journal of Critical Infrastructure Protection, Elsevier, vol. 38(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wadim Strielkowski & Andrey Vlasov & Kirill Selivanov & Konstantin Muraviev & Vadim Shakhnov, 2023. "Prospects and Challenges of the Machine Learning and Data-Driven Methods for the Predictive Analysis of Power Systems: A Review," Energies, MDPI, vol. 16(10), pages 1-31, May.
    2. Grigorii Asyaev & Alexander Sokolov & Alexey Ruchay, 2023. "Intelligent Algorithms for Event Processing and Decision Making on Information Protection Strategies against Cyberattacks," Mathematics, MDPI, vol. 11(18), pages 1-17, September.
    3. Ameni Boumaiza, 2024. "A Blockchain-Based Scalability Solution with Microgrids Peer-to-Peer Trade," Energies, MDPI, vol. 17(4), pages 1-18, February.
    4. Mousa Mohammed Khubrani & Shadab Alam, 2023. "Blockchain-Based Microgrid for Safe and Reliable Power Generation and Distribution: A Case Study of Saudi Arabia," Energies, MDPI, vol. 16(16), pages 1-34, August.
    5. Guixiang Cao & Xintong Fang & Ying Chen & Jinghuai She, 2023. "Regional Big Data Application Capability and Firm Green Technology Innovation," Sustainability, MDPI, vol. 15(17), pages 1-29, August.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wenbing Zhao & Quan Qi & Jiong Zhou & Xiong Luo, 2023. "Blockchain-Based Applications for Smart Grids: An Umbrella Review," Energies, MDPI, vol. 16(17), pages 1-35, August.
    2. Amitkumar V. Jha & Bhargav Appasani & Deepak Kumar Gupta & Bharati S. Ainapure & Nicu Bizon, 2023. "A Blockchain-Enabled Approach for Enhancing Synchrophasor Measurement in Smart Grid 3.0," Sustainability, MDPI, vol. 15(19), pages 1-20, October.
    3. Nallapaneni Manoj Kumar & Aneesh A. Chand & Maria Malvoni & Kushal A. Prasad & Kabir A. Mamun & F.R. Islam & Shauhrat S. Chopra, 2020. "Distributed Energy Resources and the Application of AI, IoT, and Blockchain in Smart Grids," Energies, MDPI, vol. 13(21), pages 1-42, November.
    4. Esmat, Ayman & de Vos, Martijn & Ghiassi-Farrokhfal, Yashar & Palensky, Peter & Epema, Dick, 2021. "A novel decentralized platform for peer-to-peer energy trading market with blockchain technology," Applied Energy, Elsevier, vol. 282(PA).
    5. Wang, Longze & Jiao, Shucen & Xie, Yu & Xia, Shiwei & Zhang, Delong & Zhang, Yan & Li, Meicheng, 2022. "Two-way dynamic pricing mechanism of hydrogen filling stations in electric-hydrogen coupling system enhanced by blockchain," Energy, Elsevier, vol. 239(PC).
    6. Sagar Hossain & Md. Rokonuzzaman & Kazi Sajedur Rahman & A. K. M. Ahasan Habib & Wen-Shan Tan & Md Mahmud & Shahariar Chowdhury & Sittiporn Channumsin, 2023. "Grid-Vehicle-Grid (G2V2G) Efficient Power Transmission: An Overview of Concept, Operations, Benefits, Concerns, and Future Challenges," Sustainability, MDPI, vol. 15(7), pages 1-24, March.
    7. Lei, Yu-Tian & Ma, Chao-Qun & Mirza, Nawazish & Ren, Yi-Shuai & Narayan, Seema Wati & Chen, Xun-Qi, 2022. "A renewable energy microgrids trading management platform based on permissioned blockchain," Energy Economics, Elsevier, vol. 115(C).
    8. Mikołaj Gwiazdowicz & Marek Natkaniec, 2023. "Feature Selection and Model Evaluation for Threat Detection in Smart Grids," Energies, MDPI, vol. 16(12), pages 1-25, June.
    9. Muhammad Waseem & Muhammad Adnan Khan & Arman Goudarzi & Shah Fahad & Intisar Ali Sajjad & Pierluigi Siano, 2023. "Incorporation of Blockchain Technology for Different Smart Grid Applications: Architecture, Prospects, and Challenges," Energies, MDPI, vol. 16(2), pages 1-29, January.
    10. Giulietti, Monica & Le Coq, Chloé & Willems, Bert & Anaya, Karim, 2019. "Smart Consumers in the Internet of Energy : Flexibility Markets & Services from Distributed Energy Resources," Other publications TiSEM 2edb43b5-bbd6-487d-abdf-7, Tilburg University, School of Economics and Management.
    11. Ahl, A. & Yarime, M. & Goto, M. & Chopra, Shauhrat S. & Kumar, Nallapaneni Manoj. & Tanaka, K. & Sagawa, D., 2020. "Exploring blockchain for the energy transition: Opportunities and challenges based on a case study in Japan," Renewable and Sustainable Energy Reviews, Elsevier, vol. 117(C).
    12. Tsao, Yu-Chung & Thanh, Vo-Van, 2021. "Toward sustainable microgrids with blockchain technology-based peer-to-peer energy trading mechanism: A fuzzy meta-heuristic approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 136(C).
    13. Marco Galici & Mario Mureddu & Emilio Ghiani & Fabrizio Pilo, 2022. "Blockchain-Based Hardware-in-the-Loop Simulation of a Decentralized Controller for Local Energy Communities," Energies, MDPI, vol. 15(20), pages 1-25, October.
    14. Lisardo Prieto González & Anna Fensel & Juan Miguel Gómez Berbís & Angela Popa & Antonio de Amescua Seco, 2021. "A Survey on Energy Efficiency in Smart Homes and Smart Grids," Energies, MDPI, vol. 14(21), pages 1-16, November.
    15. Daniel Sousa-Dias & Daniel Amyot & Ashkan Rahimi-Kian & John Mylopoulos, 2023. "A Review of Cybersecurity Concerns for Transactive Energy Markets," Energies, MDPI, vol. 16(13), pages 1-32, June.
    16. Adnan Khattak & Rasool Bukhsh & Sheraz Aslam & Ayman Yafoz & Omar Alghushairy & Raed Alsini, 2022. "A Hybrid Deep Learning-Based Model for Detection of Electricity Losses Using Big Data in Power Systems," Sustainability, MDPI, vol. 14(20), pages 1-20, October.
    17. Gao, Bixuan & Kong, Xiangyu & Li, Shangze & Chen, Yi & Zhang, Xiyuan & Liu, Ziyu & Lv, Weijia, 2024. "Enhancing anomaly detection accuracy and interpretability in low-quality and class imbalanced data: A comprehensive approach," Applied Energy, Elsevier, vol. 353(PB).
    18. Zahoor Ali Khan & Muhammad Adil & Nadeem Javaid & Malik Najmus Saqib & Muhammad Shafiq & Jin-Ghoo Choi, 2020. "Electricity Theft Detection Using Supervised Learning Techniques on Smart Meter Data," Sustainability, MDPI, vol. 12(19), pages 1-25, September.
    19. Zheyuan Sun & Sara Tavakoli & Kaveh Khalilpour & Alexey Voinov & Jonathan Paul Marshall, 2024. "Barriers to Peer-to-Peer Energy Trading Networks: A Multi-Dimensional PESTLE Analysis," Sustainability, MDPI, vol. 16(4), pages 1-23, February.
    20. Freitas Gomes, Icaro Silvestre & Perez, Yannick & Suomalainen, Emilia, 2020. "Coupling small batteries and PV generation: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 126(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jftint:v:15:y:2023:i:2:p:83-:d:1073360. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.