mlsanityData to 14 Oct 2026

Machine Learning for Intrusion Detection in Industrial Control Systems: Applications, Challenges, and Recommendations

arXiv 2202.11917 ↗First posted 24 Feb 2022cs.CR cs.LGcited by 8

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Abstract (arXiv)

Methods from machine learning are being applied to design Industrial Control Systems resilient to cyber-attacks. Such methods focus on two major areas: the 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 in use for intrusion and anomaly detection, namely, supervised, semi-supervised, unsupervised, and reinforcement learning. Literature available in the public domain was carefully selected, analyzed, and placed in a 7-dimensional space for ease of comparison. The survey is targeted at researchers, students, and practitioners. Challenges associated in using the methods and research gaps are identified and recommendations are made to fill the gaps.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 16 of its 224 references that are in the map. First posted on arXiv 24 Feb 2022.

Builds on

16 of 224 references.

References · 16

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  8. 2018Deep Continuous Clusteringcited by 21
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  10. 2018Online Cyber-Attack Detection in Smart Grid: A Reinforcement Learning Approachcited by 15
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4 more
  1. 2017Deep Sparse Subspace Clusteringcited by 7
  2. 2021Adversarial Attacks and Mitigation for Anomaly Detectors of Cyber-Physical Systemscited by 6
  3. 2021Attack Rules: An Adversarial Approach to Generate Attacks for Industrial Control Systems using Machine Learningcited by 5
  4. 2015Fast Sequence Component Analysis for Attack Detection in Synchrophasor Networkscited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 3; 2024: 1; 2025: 1; 2026: 3.

8 papers.

  1. 2023Deep transfer learning for intrusion detection in industrial control networks: A comprehensive reviewcited by 21
  2. 2023A Review of Digital Twins and their Application in Cybersecurity based on Artificial Intelligencecited by 9
  3. 2024Machine Learning-Assisted Intrusion Detection for Enhancing Internet of Things Securitycited by 1
  4. 2025Adversarial Sample Generation for Anomaly Detection in Industrial Control Systemscited by 1
  5. 2023Anomaly Detection Dataset for Industrial Control Systemscited by 0
  6. 2026Cyber-Resilient Digital Twins: Discriminating Attacks for Safe Critical Infrastructure Controlcited by 0
  7. 2026Clustering-Enhanced Domain Adaptation for Cross-Domain Intrusion Detection in Industrial Control Systemscited by 0
  8. 2026Medoid Prototype Alignment for Cross-Plant Unknown Attack Detection in Industrial Control Systemscited by 0