IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v5y2012i10p4091-4109d20814.html
   My bibliography  Save this article

Intrusion Detection of NSM Based DoS Attacks Using Data Mining in Smart Grid

Author

Listed:
  • Kyung Choi

    (Department of Computer Science and Engineering, Ewha Womans University, Seoul 120-750, Korea)

  • Xinyi Chen

    (Department of Computer Science and Engineering, Ewha Womans University, Seoul 120-750, Korea)

  • Shi Li

    (Department of Computer Science and Engineering, Ewha Womans University, Seoul 120-750, Korea)

  • Mihui Kim

    (Department of Computer Engineering, Hankyong National University, Anseong 456-749, Korea)

  • Kijoon Chae

    (Department of Computer Science and Engineering, Ewha Womans University, Seoul 120-750, Korea)

  • JungChan Na

    (Managed Security Research Team, Electronics and Telecommunications Research Institute, Daejeon 305-700, Korea)

Abstract

In this paper, we analyze the Network and System Management (NSM) requirements and NSM data objects for the intrusion detection of power systems; NSM is an IEC 62351-7 standard. We analyze a SYN flood attack and a buffer overflow attack to cause the Denial of Service (DoS) attack described in NSM. After mounting the attack in our attack testbed, we collect a data set, which is based on attributes for the attack. We then run several data mining methods with the data set using the Waikato Environment for Knowledge Analysis (WEKA). In the results, we select the decision tree algorithms with high detection rates, and choose key attributes in high level components of the trees. When we run several data mining methods again with the data set of chosen key attributes, the detection rates of most data mining methods are higher than before. We prove that our selected attack attributes, and the proposed detection process, are efficient and suitable for intrusion detection in the smart grid environment.

Suggested Citation

  • Kyung Choi & Xinyi Chen & Shi Li & Mihui Kim & Kijoon Chae & JungChan Na, 2012. "Intrusion Detection of NSM Based DoS Attacks Using Data Mining in Smart Grid," Energies, MDPI, vol. 5(10), pages 1-19, October.
    • Handle: RePEc:gam:jeners:v:5:y:2012:i:10:p:4091-4109:d:20814
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/5/10/4091/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/5/10/4091/
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Neetesh Saxena & Bong Jun Choi, 2015. "State of the Art Authentication, Access Control, and Secure Integration in Smart Grid," Energies, MDPI, vol. 8(10), pages 1-33, October.
    2. Jianlei Gao & Senchun Chai & Baihai Zhang & Yuanqing Xia, 2019. "Research on Network Intrusion Detection Based on Incremental Extreme Learning Machine and Adaptive Principal Component Analysis," Energies, MDPI, vol. 12(7), pages 1-17, March.
    3. 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.
    4. Matthew Boeding & Kelly Boswell & Michael Hempel & Hamid Sharif & Juan Lopez & Kalyan Perumalla, 2022. "Survey of Cybersecurity Governance, Threats, and Countermeasures for the Power Grid," Energies, MDPI, vol. 15(22), pages 1-22, November.

    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:5:y:2012:i:10:p:4091-4109:d:20814. 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: 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.