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Extending the cyber-attack landscape for SCADA-based critical infrastructure

Author

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  • Rodofile, Nicholas R.
  • Radke, Kenneth
  • Foo, Ernest

Abstract

The move from point-to-point serial communication to traditional information technology (IT) networks has created new challenges in providing cyber-security for supervisory control and data acquisition (SCADA) systems in critical infrastructure. Current research on the attack landscape for critical infrastructure concentrates on either IT-based or protocol specific attacks. However, there is limited research focus on “the bigger picture”, the combination of IT attacks and critical infrastructure protocol attacks, and little consideration of cyber-attacks targeting an entire (SCADA)-based critical infrastructure system. Due to such narrow research, there is a complete lack of focus when comprehending full-scale cyber attacks on SCADA-based critical infrastructure systems. As a result, new attacks combining various vulnerabilities in engineering systems and IT systems are yet to be discovered.

Suggested Citation

  • Rodofile, Nicholas R. & Radke, Kenneth & Foo, Ernest, 2019. "Extending the cyber-attack landscape for SCADA-based critical infrastructure," International Journal of Critical Infrastructure Protection, Elsevier, vol. 25(C), pages 14-35.
    • Handle: RePEc:eee:ijocip:v:25:y:2019:i:c:p:14-35
    DOI: 10.1016/j.ijcip.2019.01.002
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    References listed on IDEAS

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    1. Knowles, William & Prince, Daniel & Hutchison, David & Disso, Jules Ferdinand Pagna & Jones, Kevin, 2015. "A survey of cyber security management in industrial control systems," International Journal of Critical Infrastructure Protection, Elsevier, vol. 9(C), pages 52-80.
    2. Huitsing, Peter & Chandia, Rodrigo & Papa, Mauricio & Shenoi, Sujeet, 2008. "Attack taxonomies for the Modbus protocols," International Journal of Critical Infrastructure Protection, Elsevier, vol. 1(C), pages 37-44.
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    Cited by:

    1. Ajit Kumar & Neetesh Saxena & Souhwan Jung & Bong Jun Choi, 2021. "Improving Detection of False Data Injection Attacks Using Machine Learning with Feature Selection and Oversampling," Energies, MDPI, vol. 15(1), pages 1-22, December.
    2. Li, Xueping & Wang, Yaokun & Lu, Zhigang, 2023. "Graph-based detection for false data injection attacks in power grid," Energy, Elsevier, vol. 263(PC).

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