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Using timing-based side channels for anomaly detection in industrial control systems

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  • Dunlap, Stephen
  • Butts, Jonathan
  • Lopez, Juan
  • Rice, Mason
  • Mullins, Barry

Abstract

The critical infrastructure, which includes the electric power grid, railroads and water treatment facilities, is dependent on the proper operation of industrial control systems. However, malware such as Stuxnet has demonstrated the ability to alter industrial control system parameters to create physical effects. Of particular concern is malware that targets embedded devices that monitor and control system functionality, while masking the actions from plant operators and security analysts. Indeed, system security relies on guarantees that the assurance of these devices can be maintained throughout their lifetimes. This paper presents a novel approach that uses timing-based side channel analysis to establish a unique device fingerprint that helps detect unauthorized modifications of the device. The approach is applied to an Allen Bradley ControlLogix programmable logic controller where execution time measurements are collected and analyzed by a custom anomaly detection system to detect abnormal behavior. The anomaly detection system achieves true positive rates of 0.978–1.000 with false positive rates of 0.033–0.044. The test results demonstrate the feasibility of using timing-based side channel analysis to detect anomalous behavior in programmable logic controllers.

Suggested Citation

  • Dunlap, Stephen & Butts, Jonathan & Lopez, Juan & Rice, Mason & Mullins, Barry, 2016. "Using timing-based side channels for anomaly detection in industrial control systems," International Journal of Critical Infrastructure Protection, Elsevier, vol. 15(C), pages 12-26.
  • Handle: RePEc:eee:ijocip:v:15:y:2016:i:c:p:12-26
    DOI: 10.1016/j.ijcip.2016.07.003
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    3. Stone, Samuel & Temple, Michael, 2012. "Radio-frequency-based anomaly detection for programmable logic controllers in the critical infrastructure," International Journal of Critical Infrastructure Protection, Elsevier, vol. 5(2), pages 66-73.
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    Cited by:

    1. Jie, Xinchun & Wang, Haikuan & Fei, Minrui & Du, Dajun & Sun, Qing & Yang, T.C., 2018. "Anomaly behavior detection and reliability assessment of control systems based on association rules," International Journal of Critical Infrastructure Protection, Elsevier, vol. 22(C), pages 90-99.
    2. Saraeian, Shideh & Shirazi, Babak & Motameni, Homayun, 2019. "Adaptive control of criticality infrastructure in automatic closed-loop supply chain considering uncertainty," International Journal of Critical Infrastructure Protection, Elsevier, vol. 25(C), pages 102-124.

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