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Detecting Cyberattacks in Industrial Control Systems Using Convolutional Neural Networks

arXiv 1806.08110 ↗First posted 21 Jun 2018cs.CR cs.LGcited by 23

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Abstract (arXiv)

This paper presents a study on detecting cyberattacks on industrial control systems (ICS) using unsupervised deep neural networks, specifically, convolutional neural networks. The study was performed on a SecureWater Treatment testbed (SWaT) dataset, which represents a scaled-down version of a real-world industrial water treatment plant. e suggest a method for anomaly detection based on measuring the statistical deviation of the predicted value from the observed value.We applied the proposed method by using a variety of deep neural networks architectures including different variants of convolutional and recurrent networks. The test dataset from SWaT included 36 different cyberattacks. The proposed method successfully detects the vast majority of the attacks with a low false positive rate thus improving on previous works based on this data set. The results of the study show that 1D convolutional networks can be successfully applied to anomaly detection in industrial control systems and outperform more complex recurrent networks while being much smaller and faster to train.

Key facts. Cited by 23 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Jun 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 3; 2022: 1; 2023: 7; 2024: 2; 2025: 4; 2026: 6.

23 papers.

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  1. 2021Unsupervised Deep Anomaly Detection for Multi-Sensor Time-Series Signalscited by 41
  2. 2021Adversarial Attacks and Mitigation for Anomaly Detectors of Cyber-Physical Systemscited by 6
  3. 2023Digital Twin-based Anomaly Detection with Curriculum Learning in Cyber-physical Systemscited by 6
  4. 2024How Deep is your Guess? A Fresh Perspective on Deep Learning for Medical Time-Series Imputationcited by 5
  5. 2025Causal Digital Twins for Cyber-Physical Security: A Framework for Robust Anomaly Detection in Industrial Control Systemscited by 4
  6. 2023Deep Graph Stream SVDD: Anomaly Detection in Cyber-Physical Systemscited by 1
  7. 2023CSAI: Conditional Self-Attention Imputation for Healthcare Time-seriescited by 1
  8. 2025Runtime Anomaly Detection for Drones: An Integrated Rule-Mining and Unsupervised-Learning Approachcited by 1
  9. 2025Adversarial Sample Generation for Anomaly Detection in Industrial Control Systemscited by 1
  10. 2021Code Integrity Attestation for PLCs using Black Box Neural Network Predictionscited by 0
  11. 2022Recurrent Auto-Encoder With Multi-Resolution Ensemble and Predictive Coding for Multivariate Time-Series Anomaly Detectioncited by 0
  12. 2023Few-shot Detection of Anomalies in Industrial Cyber-Physical System via Prototypical Network and Contrastive Learningcited by 0
11 more
  1. 2023Two-phase Dual COPOD Method for Anomaly Detection in Industrial Control Systemcited by 0
  2. 2023Evaluation Strategy of Time-series Anomaly Detection with Decay Functioncited by 0
  3. 2023Electrical Grid Anomaly Detection via Tensor Decompositioncited by 0
  4. 2024An Attention-Based Deep Generative Model for Anomaly Detection in Industrial Control Systemscited by 0
  5. 2025OIPR: Evaluation for Time-series Anomaly Detection Inspired by Operator Interestcited by 0
  6. 2026Spatio-Temporal Attention Graph Neural Network: Explaining Causalities With Attentioncited by 0
  7. 2026PhaseNet++: Phase-Aware Frequency-Domain Anomaly Detection for Industrial Control Systems via Phase Coherence Graphscited by 0
  8. 2026Attack Detection using Time Series Foundation Modelscited by 0
  9. 2026Digital Twin Degradation: Detecting Cyber Physical Attacks via Temporal Inconsistenciescited by 0
  10. 2026Robustness of Anomaly Detection Models for Industrial Control Systems under Training-Time Data Contaminationcited by 0
  11. 2026Physics-Attested Federated Learning: Securing Collaborative Anomaly Detection in Critical Water Infrastructurecited by 0