IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i13p2493-d243727.html
   My bibliography  Save this article

Coordinated Defense of Distributed Denial of Service Attacks against the Multi-Area Load Frequency Control Services

Author

Listed:
  • Qi Wang

    (School of Electrical Engineering, Southeast University, Nanjing 210000, China)

  • Wei Tai

    (School of Electrical Engineering, Southeast University, Nanjing 210000, China)

  • Yi Tang

    (School of Electrical Engineering, Southeast University, Nanjing 210000, China)

  • Hong Zhu

    (State Grid Nanjing Power Supply Company, Nanjing 210000, China)

  • Ming Zhang

    (State Grid Nanjing Power Supply Company, Nanjing 210000, China)

  • Dongxu Zhou

    (State Grid Nanjing Power Supply Company, Nanjing 210000, China)

Abstract

With the application of information and communication technology (ICT), the modern power system has gradually been updated to a typical cyber physical power system (CPPS). The deployment of distributed measurement devices enriches the application range of power communication services. However, due to the easy accessibility of distributed devices, it also creates favorable conditions for distributed denial-of-service (DDoS) attacks. In this paper, we focus on the security performance and defense strategies of the CPPS against DDoS attacks. In order to construct a coordinated defense in the power and information space, the cyber-attack process with a complete data flow of the CPPS needs to be described precisely. Therefore, a co-simulation technology-based platform is utilized to coordinate various layers in the CPPS and provide a unified research tool for the attack-defense test. On this basis, OPNET is used to replicate DDoS attacks in the information layer. Then, through the load frequency control (LFC) service of a multi-area interconnected power system, the influence of delays resulting from attacks on the control effect of the power layer is analyzed. Finally, to cope with the attack effects in both layers, detection measures of information and recovery measures of power quality are coordinated to eliminate attack consequences. Therefore, the stable operation of power services can be enabled.

Suggested Citation

  • Qi Wang & Wei Tai & Yi Tang & Hong Zhu & Ming Zhang & Dongxu Zhou, 2019. "Coordinated Defense of Distributed Denial of Service Attacks against the Multi-Area Load Frequency Control Services," Energies, MDPI, vol. 12(13), pages 1-19, June.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:13:p:2493-:d:243727
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/13/2493/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/13/2493/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sergey V. Buldyrev & Roni Parshani & Gerald Paul & H. Eugene Stanley & Shlomo Havlin, 2010. "Catastrophic cascade of failures in interdependent networks," Nature, Nature, vol. 464(7291), pages 1025-1028, April.
    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. Athira M. Mohan & Nader Meskin & Hasan Mehrjerdi, 2020. "A Comprehensive Review of the Cyber-Attacks and Cyber-Security on Load Frequency Control of Power Systems," Energies, MDPI, vol. 13(15), pages 1-33, July.
    2. Tania Wallis & Rafał Leszczyna, 2022. "EE-ISAC—Practical Cybersecurity Solution for the Energy Sector," Energies, MDPI, vol. 15(6), pages 1-23, March.
    3. Rumpa Dasgupta & Amin Sakzad & Carsten Rudolph, 2021. "Cyber Attacks in Transactive Energy Market-Based Microgrid Systems," Energies, MDPI, vol. 14(4), pages 1-17, February.

    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. Wang, Chengjiang & Wang, Li & Wang, Juan & Sun, Shiwen & Xia, Chengyi, 2017. "Inferring the reputation enhances the cooperation in the public goods game on interdependent lattices," Applied Mathematics and Computation, Elsevier, vol. 293(C), pages 18-29.
    2. Tang, Liang & Jing, Ke & He, Jie & Stanley, H. Eugene, 2016. "Robustness of assembly supply chain networks by considering risk propagation and cascading failure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 459(C), pages 129-139.
    3. Shogo Mizutaka & Kousuke Yakubo, 2017. "Structural instability of large-scale functional networks," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-11, July.
    4. Balint, T. & Lamperti, F. & Mandel, A. & Napoletano, M. & Roventini, A. & Sapio, A., 2017. "Complexity and the Economics of Climate Change: A Survey and a Look Forward," Ecological Economics, Elsevier, vol. 138(C), pages 252-265.
    5. Fridgen, Gilbert & Keller, Robert & Körner, Marc-Fabian & Schöpf, Michael, 2020. "A holistic view on sector coupling," Energy Policy, Elsevier, vol. 147(C).
    6. Hernandez-Fajardo, Isaac & Dueñas-Osorio, Leonardo, 2013. "Probabilistic study of cascading failures in complex interdependent lifeline systems," Reliability Engineering and System Safety, Elsevier, vol. 111(C), pages 260-272.
    7. Yu, Haitao & Wang, Jiang & Liu, Chen & Deng, Bin & Wei, Xile, 2014. "Delay-induced synchronization transitions in modular scale-free neuronal networks with hybrid electrical and chemical synapses," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 405(C), pages 25-34.
    8. Sgrignoli, Paolo & Metulini, Rodolfo & Schiavo, Stefano & Riccaboni, Massimo, 2015. "The relation between global migration and trade networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 417(C), pages 245-260.
    9. Zhou, Yaoming & Wang, Junwei, 2018. "Efficiency of complex networks under failures and attacks: A percolation approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 658-664.
    10. Monsalve, Mauricio & de la Llera, Juan Carlos, 2019. "Data-driven estimation of interdependencies and restoration of infrastructure systems," Reliability Engineering and System Safety, Elsevier, vol. 181(C), pages 167-180.
    11. Liu, Huan & Tatano, Hirokazu & Pflug, Georg & Hochrainer-Stigler, Stefan, 2021. "Post-disaster recovery in industrial sectors: A Markov process analysis of multiple lifeline disruptions," Reliability Engineering and System Safety, Elsevier, vol. 206(C).
    12. Liu, Run-Ran & Chu, Changchang & Meng, Fanyuan, 2023. "Higher-order interdependent percolation on hypergraphs," Chaos, Solitons & Fractals, Elsevier, vol. 177(C).
    13. Krawiecki, A., 2018. "Spin glass transition in a simple variant of the Ising model on multiplex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 773-790.
    14. Shekhtman, Louis M. & Danziger, Michael M. & Havlin, Shlomo, 2016. "Recent advances on failure and recovery in networks of networks," Chaos, Solitons & Fractals, Elsevier, vol. 90(C), pages 28-36.
    15. Weihua Lei & Luiz G. A. Alves & Luís A. Nunes Amaral, 2022. "Forecasting the evolution of fast-changing transportation networks using machine learning," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    16. Wang, Gang-Jin & Chen, Yang-Yang & Si, Hui-Bin & Xie, Chi & Chevallier, Julien, 2021. "Multilayer information spillover networks analysis of China’s financial institutions based on variance decompositions," International Review of Economics & Finance, Elsevier, vol. 73(C), pages 325-347.
    17. Leto Peel & Tiago P. Peixoto & Manlio De Domenico, 2022. "Statistical inference links data and theory in network science," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    18. Kyu-Min Lee & Kwang-Il Goh, 2016. "Strength of weak layers in cascading failures on multiplex networks: case of the international trade network," Papers 1603.05181, arXiv.org, revised May 2016.
    19. Leonardo Massai & Giacomo Como & Fabio Fagnani, 2019. "Equilibria and Systemic Risk in Saturated Networks," Papers 1912.04815, arXiv.org, revised Jan 2021.
    20. Zhu, Qian & Zhu, Zhiliang & Wang, Yifan & Yu, Hai, 2016. "Fuzzy-information-based robustness of interconnected networks against attacks and failures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 458(C), pages 194-203.

    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:jeners:v:12:y:2019:i:13:p:2493-:d:243727. 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.