IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v30y2019i3d10.1007_s10845-017-1315-5.html
   My bibliography  Save this article

Detecting cyber-physical attacks in CyberManufacturing systems with machine learning methods

Author

Listed:
  • Mingtao Wu

    (Syracuse University)

  • Zhengyi Song

    (Syracuse University)

  • Young B. Moon

    (Syracuse University)

Abstract

CyberManufacturing system (CMS) is a vision for future manufacturing systems. The concept delineates a vision of advanced manufacturing system integrated with technologies such as Internet of Things, Cloud Computing, Sensors Network and Machine Learning. As a result, cyber-attacks such as Stuxnet attack will increase along with growing simultaneous connectivity. Now, cyber-physical attacks are new and unique risks to CMSs and modern cyber security countermeasure is not enough. To learn this new vulnerability, the cyber-physical attacks is defined via a taxonomy under the vision of CMS. Machine learning on physical data is studied for detecting cyber-physical attacks. Two examples were developed with simulation and experiments: 3D printing malicious attack and CNC milling machine malicious attack. By implementing machine learning methods in physical data, the anomaly detection algorithm reached 96.1% accuracy in detecting cyber-physical attacks in 3D printing process; random forest algorithm reached on average 91.1% accuracy in detecting cyber-physical attacks in CNC milling process.

Suggested Citation

  • Mingtao Wu & Zhengyi Song & Young B. Moon, 2019. "Detecting cyber-physical attacks in CyberManufacturing systems with machine learning methods," Journal of Intelligent Manufacturing, Springer, vol. 30(3), pages 1111-1123, March.
    • Handle: RePEc:spr:joinma:v:30:y:2019:i:3:d:10.1007_s10845-017-1315-5
    DOI: 10.1007/s10845-017-1315-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-017-1315-5
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10845-017-1315-5?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

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


    Cited by:

    1. Zhangyue Shi & Abdullah Al Mamun & Chen Kan & Wenmeng Tian & Chenang Liu, 2023. "An LSTM-autoencoder based online side channel monitoring approach for cyber-physical attack detection in additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 34(4), pages 1815-1831, April.
    2. Ranabhat, Bikash & Clements, Joseph & Gatlin, Jacob & Hsiao, Kuang-Ting & Yampolskiy, Mark, 2019. "Optimal sabotage attack on composite material parts," International Journal of Critical Infrastructure Protection, Elsevier, vol. 26(C).
    3. Xiaobao Zhu & Jing Shi & Fengjie Xie & Rouqi Song, 2020. "Pricing strategy and system performance in a cloud-based manufacturing system built on blockchain technology," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 1985-2002, December.
    4. Safari, Mohammad & Parvinnia, Elham & Haddad, Alireza Keshavarz, 2021. "Industrial intrusion detection based on the behavior of rotating machine," International Journal of Critical Infrastructure Protection, Elsevier, vol. 34(C).
    5. Ying Zhang & Mutahar Safdar & Jiarui Xie & Jinghao Li & Manuel Sage & Yaoyao Fiona Zhao, 2023. "A systematic review on data of additive manufacturing for machine learning applications: the data quality, type, preprocessing, and management," Journal of Intelligent Manufacturing, Springer, vol. 34(8), pages 3305-3340, December.
    6. Zhao Peng & Huan Zhang & Hongtao Tang & Yue Feng & Weiming Yin, 2022. "Research on flexible job-shop scheduling problem in green sustainable manufacturing based on learning effect," Journal of Intelligent Manufacturing, Springer, vol. 33(6), pages 1725-1746, August.
    7. Saheed, Yakub Kayode & Abdulganiyu, Oluwadamilare Harazeem & Majikumna, Kaloma Usman & Mustapha, Musa & Workneh, Abebaw Degu, 2024. "ResNet50-1D-CNN: A new lightweight resNet50-One-dimensional convolution neural network transfer learning-based approach for improved intrusion detection in cyber-physical systems," International Journal of Critical Infrastructure Protection, Elsevier, vol. 45(C).
    8. William Derigent & Olivier Cardin & Damien Trentesaux, 2021. "Industry 4.0: contributions of holonic manufacturing control architectures and future challenges," Journal of Intelligent Manufacturing, Springer, vol. 32(7), pages 1797-1818, October.
    9. Md Doulotuzzaman Xames & Fariha Kabir Torsha & Ferdous Sarwar, 2023. "A systematic literature review on recent trends of machine learning applications in additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 34(6), pages 2529-2555, August.

    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:spr:joinma:v:30:y:2019:i:3:d:10.1007_s10845-017-1315-5. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.