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Detecting control system misbehavior by fingerprinting programmable logic controller functionality

Author

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  • Stockman, Melissa
  • Dwivedi, Dipankar
  • Gentz, Reinhard
  • Peisert, Sean

Abstract

In recent years, attacks such as the Stuxnet malware have demonstrated that cyberattacks against control systems cause extensive damage. These attacks can result in physical damage to the networked systems under their control. In this paper, we discuss our approach for detecting such attacks by distinguishing between programs running on a programmable logic controller (PLC) without having to monitor communications. Using power signatures generated by an attached, high-frequency power measurement device, we can identify what a PLC is doing and when an attack may have altered what the PLC should be doing. To accomplish this, we generated labeled data for testing our methods and applied feature engineering techniques and machine learning models. The results demonstrate that Random Forests and Convolutional Neural Networks classify programs with up to 98% accuracy for major program differences and 84% accuracy for minor differences. Our results can be used for both online and offline applications.

Suggested Citation

  • Stockman, Melissa & Dwivedi, Dipankar & Gentz, Reinhard & Peisert, Sean, 2019. "Detecting control system misbehavior by fingerprinting programmable logic controller functionality," International Journal of Critical Infrastructure Protection, Elsevier, vol. 26(C).
    • Handle: RePEc:eee:ijocip:v:26:y:2019:i:c:s1874548219300320
    DOI: 10.1016/j.ijcip.2019.100306
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    Cited by:

    1. Umer, Muhammad Azmi & Junejo, Khurum Nazir & Jilani, Muhammad Taha & Mathur, Aditya P., 2022. "Machine learning for intrusion detection in industrial control systems: Applications, challenges, and recommendations," International Journal of Critical Infrastructure Protection, Elsevier, vol. 38(C).

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