IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i13p5206-d1188340.html
   My bibliography  Save this article

Review of Cyberattack Implementation, Detection, and Mitigation Methods in Cyber-Physical Systems

Author

Listed:
  • Namhla Mtukushe

    (School of Electrical and Information Engineering, University of the Witwatersrand, Johannesburg 2000, South Africa
    Department of Electrical Power Engineering, Durban University of Technology, Durban 4000, South Africa)

  • Adeniyi K. Onaolapo

    (Department of Electrical and Electronics Engineering Technology, University of Johannesburg, Johannesburg 2092, South Africa)

  • Anuoluwapo Aluko

    (Power Research Laboratory, Department of Electrical and Software Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada)

  • David G. Dorrell

    (School of Electrical and Information Engineering, University of the Witwatersrand, Johannesburg 2000, South Africa)

Abstract

With the rapid proliferation of cyber-physical systems (CPSs) in various sectors, including critical infrastructure, transportation, healthcare, and the energy industry, there is a pressing need for robust cybersecurity mechanisms to protect these systems from cyberattacks. A cyber-physical system is a combination of physical and cyber components, and a security breach in either component can lead to catastrophic consequences. Cyberattack detection and mitigation methods in CPSs involve the use of various techniques such as intrusion detection systems (IDSs), firewalls, access control mechanisms, and encryption. Overall, effective cyberattack detection and mitigation methods in CPSs require a comprehensive security strategy that considers the unique characteristics of a CPS, such as the interconnectedness of physical and cyber components, the need for real-time response, and the potential consequences of a security breach. By implementing these methods, CPSs can be better protected against cyberattacks, thus ensuring the safety and reliability of critical infrastructure and other vital systems. This paper reviews the various kinds of cyber-attacks that have been launched or implemented in CPSs. It reports on the state-of-the-art detection and mitigation methods that have been used or proposed to secure the safe operation of various CPSs. A summary of the requirements that CPSs need to satisfy their operation is highlighted, and an analysis of the benefits and drawbacks of model-based and data-driven techniques is carried out. The roles of machine learning in cyber assault are reviewed. In order to direct future study and motivate additional investigation of this increasingly important subject, some challenges that have been unaddressed, such as the prerequisites for CPSs, an in-depth analysis of CPS characteristics and requirements, and the creation of a holistic review of the different kinds of attacks on different CPSs, together with detection and mitigation algorithms, are discussed in this review.

Suggested Citation

  • Namhla Mtukushe & Adeniyi K. Onaolapo & Anuoluwapo Aluko & David G. Dorrell, 2023. "Review of Cyberattack Implementation, Detection, and Mitigation Methods in Cyber-Physical Systems," Energies, MDPI, vol. 16(13), pages 1-25, July.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:13:p:5206-:d:1188340
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/13/5206/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/13/5206/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yang, Li & Cao, Xiedong & Li, Jie, 2016. "A new cyber security risk evaluation method for oil and gas SCADA based on factor state space," Chaos, Solitons & Fractals, Elsevier, vol. 89(C), pages 203-209.
    2. Zhang, Ning & Qi, Wenhai & Pang, Guocheng & Cheng, Jun & Shi, Kaibo, 2022. "Observer-based sliding mode control for fuzzy stochastic switching systems with deception attacks," Applied Mathematics and Computation, Elsevier, vol. 427(C).
    3. Adeniyi Kehinde Onaolapo & Rudiren Pillay Carpanen & David George Dorrell & Evans Eshiemogie Ojo, 2022. "A Comparative Assessment of Conventional and Artificial Neural Networks Methods for Electricity Outage Forecasting," Energies, MDPI, vol. 15(2), pages 1-21, January.
    4. Sanaa Kaddoura & Ramzi A. Haraty & Karam Al Kontar & Omar Alfandi, 2021. "A Parallelized Database Damage Assessment Approach after Cyberattack for Healthcare Systems," Future Internet, MDPI, vol. 13(4), pages 1-18, March.
    5. Marilena Stănculescu & Sorin Deleanu & Paul Cristian Andrei & Horia Andrei, 2021. "A Case Study of an Industrial Power Plant under Cyberattack: Simulation and Analysis," Energies, MDPI, vol. 14(9), pages 1-20, April.
    6. Saeed Ahmed & YoungDoo Lee & Seung-Ho Hyun & Insoo Koo, 2019. "Mitigating the Impacts of Covert Cyber Attacks in Smart Grids Via Reconstruction of Measurement Data Utilizing Deep Denoising Autoencoders," Energies, MDPI, vol. 12(16), pages 1-24, August.
    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. Adeniyi K. Onaolapo & Gulshan Sharma & Pitshou N. Bokoro & Anuoluwapo Aluko & Giovanni Pau, 2023. "A Distributed Control Scheme for Cyber-Physical DC Microgrid Systems," Energies, MDPI, vol. 16(15), pages 1-17, July.

    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. Sanaa Kaddoura, 2022. "Evaluation of Machine Learning Algorithm on Drinking Water Quality for Better Sustainability," Sustainability, MDPI, vol. 14(18), pages 1-18, September.
    2. 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.
    3. Ntumba Marc-Alain Mutombo & Bubele Papy Numbi, 2022. "Development of a Linear Regression Model Based on the Most Influential Predictors for a Research Office Cooling Load," Energies, MDPI, vol. 15(14), pages 1-20, July.
    4. Anupama Mishra & Neena Gupta & Brij B. Gupta, 2023. "Defensive mechanism against DDoS attack based on feature selection and multi-classifier algorithms," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 82(2), pages 229-244, February.
    5. Efstathios Kontouras & Anthony Tzes & Leonidas Dritsas, 2019. "Hybrid Detection of Intermittent Cyber-Attacks in Networked Power Systems," Energies, MDPI, vol. 12(24), pages 1-29, December.
    6. Ulvi Rzazade & Sergey Deryabin & Igor Temkin & Egor Kondratev & Alexander Ivannikov, 2023. "On the Issue of the Creation and Functioning of Energy Efficiency Management Systems for Technological Processes of Mining Enterprises," Energies, MDPI, vol. 16(13), pages 1-21, June.
    7. Mustafa Saglam & Catalina Spataru & Omer Ali Karaman, 2023. "Forecasting Electricity Demand in Turkey Using Optimization and Machine Learning Algorithms," Energies, MDPI, vol. 16(11), pages 1-23, June.
    8. Paul Cristian Andrei & Horia Andrei, 2022. "Power Systems’ Connectivity and Resiliency: Modeling, Simulation and Analysis," Energies, MDPI, vol. 15(8), pages 1-3, April.
    9. Alessandro Midolo & Emiliano Tramontana, 2023. "An Automatic Transformer from Sequential to Parallel Java Code," Future Internet, MDPI, vol. 15(9), pages 1-19, September.
    10. Dong, Lewei & Xu, Huiling & Zhang, Liming & Li, Zhengcai & Chen, Yuqing, 2023. "Adjustable proportional-integral multivariable observer-based FDI attack dynamic reconstitution and secure control for cyber-physical systems," Applied Mathematics and Computation, Elsevier, vol. 443(C).
    11. 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).
    12. Kumari, Pooja & Shankar, Amit & Behl, Abhishek & Pereira, Vijay & Yahiaoui, Dorra & Laker, Benjamin & Gupta, Brij B. & Arya, Varsha, 2024. "Investigating the barriers towards adoption and implementation of open innovation in healthcare," Technological Forecasting and Social Change, Elsevier, vol. 200(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:jeners:v:16:y:2023:i:13:p:5206-:d:1188340. 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.