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Machine learning for intrusion detection in industrial control systems: Applications, challenges, and recommendations

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  • Umer, Muhammad Azmi
  • Junejo, Khurum Nazir
  • Jilani, Muhammad Taha
  • Mathur, Aditya P.

Abstract

Methods from machine learning are used in the design of secure Industrial Control Systems. Such methods focus on two major areas: detection of intrusions at the network level using the information acquired through network packets, and detection of anomalies at the physical process level using data that represents the physical behavior of the system. This survey focuses on four types of methods from machine learning for intrusion and anomaly detection, namely, supervised, semi-supervised, unsupervised, and reinforcement learning. The literature available in the public domain was carefully selected, analyzed, and placed along a 10-dimensional space for ease of comparison. This multi-dimensional approach is found valuable in the comparison of the methods considered and enables a scientific discussion on their utility in specific environments. The challenges associated in using machine learning, and gaps in research, are identified and recommendations made.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:ijocip:v:38:y:2022:i:c:s1874548222000087
    DOI: 10.1016/j.ijcip.2022.100516
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    References listed on IDEAS

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    1. Matti Mantere & Mirko Sailio & Sami Noponen, 2013. "Network Traffic Features for Anomaly Detection in Specific Industrial Control System Network," Future Internet, MDPI, vol. 5(4), pages 1-14, September.
    2. Sugumar, Gayathri & Mathur, Aditya, 2019. "A method for testing distributed anomaly detectors," International Journal of Critical Infrastructure Protection, Elsevier, vol. 27(C).
    3. Umer, Muhammad Azmi & Mathur, Aditya & Junejo, Khurum Nazir & Adepu, Sridhar, 2020. "Generating invariants using design and data-centric approaches for distributed attack detection," International Journal of Critical Infrastructure Protection, Elsevier, vol. 28(C).
    4. Jia, Yifan & Wang, Jingyi & Poskitt, Christopher M. & Chattopadhyay, Sudipta & Sun, Jun & Chen, Yuqi, 2021. "Adversarial attacks and mitigation for anomaly detectors of cyber-physical systems," International Journal of Critical Infrastructure Protection, Elsevier, vol. 34(C).
    5. 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).
    6. Morris, Thomas & Srivastava, Anurag & Reaves, Bradley & Gao, Wei & Pavurapu, Kalyan & Reddi, Ram, 2011. "A control system testbed to validate critical infrastructure protection concepts," International Journal of Critical Infrastructure Protection, Elsevier, vol. 4(2), pages 88-103.
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    Cited by:

    1. Etxezarreta, Xabier & Garitano, Iñaki & Iturbe, Mikel & Zurutuza, Urko, 2023. "Software-Defined Networking approaches for intrusion response in Industrial Control Systems: A survey," International Journal of Critical Infrastructure Protection, Elsevier, vol. 42(C).

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