mlsanityData to 14 Oct 2026

Multivariate Industrial Time Series with Cyber-Attack Simulation: Fault Detection Using an LSTM-based Predictive Data Model

arXiv 1612.06676 ↗First posted 20 Dec 2016cs.LG stat.MLcited by 34

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Abstract (arXiv)

We adopted an approach based on an LSTM neural network to monitor and detect faults in industrial multivariate time series data. To validate the approach we created a Modelica model of part of a real gasoil plant. By introducing hacks into the logic of the Modelica model, we were able to generate both the roots and causes of fault behavior in the plant. Having a self-consistent data set with labeled faults, we used an LSTM architecture with a forecasting error threshold to obtain precision and recall quality metrics. The dependency of the quality metric on the threshold level is considered. An appropriate mechanism such as "one handle" was introduced for filtering faults that are outside of the plant operator field of interest.

Key facts. Cited by 34 papers in the map (data through 14 Oct 2026). First posted on arXiv 20 Dec 2016.

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Citing papers per year: 2021: 7; 2022: 6; 2023: 2; 2024: 4; 2025: 8; 2026: 7.

34 papers.

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  1. 2021Graph Neural Network-Based Anomaly Detection in Multivariate Time Seriescited by 301
  2. 2022Deep Learning for Time Series Anomaly Detection: A Surveycited by 131
  3. 2021Learning Graph Structures with Transformer for Multivariate Time Series Anomaly Detection in IoTcited by 104
  4. 2025TAB: Unified Benchmarking of Time Series Anomaly Detection Methodscited by 60
  5. 2022Adaptive Memory Networks with Self-supervised Learning for Unsupervised Anomaly Detectioncited by 18
  6. 2022Detecting Anomalies within Time Series using Local Neural Transformationscited by 12
  7. 2026TSAQA: Time Series Analysis Question And Answering Benchmarkcited by 11
  8. 2025Time-RA: Towards Time Series Reasoning for Anomaly Diagnosis with LLM Feedbackcited by 8
  9. 2021Weakly Supervised Temporal Anomaly Segmentation with Dynamic Time Warpingcited by 7
  10. 2024AAD-LLM: Adaptive Anomaly Detection Using Large Language Modelscited by 7
  11. 2025mTSBench: Benchmarking Multivariate Time Series Anomaly Detection and Model Selection at Scalecited by 7
  12. 2021Adversarial Attacks and Mitigation for Anomaly Detectors of Cyber-Physical Systemscited by 6
22 more
  1. 2024Online Model-based Anomaly Detection in Multivariate Time Series: Taxonomy, Survey, Research Challenges and Future Directionscited by 6
  2. 2025xLSTMAD: A Powerful xLSTM-based Method for Anomaly Detectioncited by 6
  3. 2023Deep Anomaly Detection on Tennessee Eastman Process Datacited by 5
  4. 2025MLLM4TS: Leveraging Vision and Multimodal Language Models for General Time-Series Analysiscited by 5
  5. 2024CAFO: Feature-Centric Explanation on Time Series Classificationcited by 4
  6. 2025RAAD-LLM: Adaptive Anomaly Detection Using LLMs and RAG Integrationcited by 4
  7. 2022Hybridization of Capsule and LSTM Networks for unsupervised anomaly detection on multivariate datacited by 2
  8. 2022Anomaly Detection for Multivariate Time Series on Large-scale Fluid Handling Plant Using Two-stage Autoencodercited by 2
  9. 2025MSAD: A Deep Dive into Model Selection for Time series Anomaly Detectioncited by 2
  10. 2021Anomaly Detection in Beehives: An Algorithm Comparisoncited by 1
  11. 2026ASTER: Latent Pseudo-Anomaly Generation for Unsupervised Time-Series Anomaly Detectioncited by 1
  12. 2021Modelling Neuronal Behaviour with Time Series Regression: Recurrent Neural Networks on C. Elegans Datacited by 0
  13. 2021Memory-augmented Adversarial Autoencoders for Multivariate Time-series Anomaly Detection with Deep Reconstruction and Predictioncited by 0
  14. 2022Data augmentation through multivariate scenario forecasting in Data Centers using Generative Adversarial Networkscited by 0
  15. 2023AlerTiger: Deep Learning for AI Model Health Monitoring at LinkedIncited by 0
  16. 2024Accurate and fast anomaly detection in industrial processes and IoT environmentscited by 0
  17. 2025Reasoning-based Anomaly Detection Framework: A Real-time, Scalable, and Automated Approach to Anomaly Detection Across Domainscited by 0
  18. 2026Temporal-Conditioned Normalizing Flows for Multivariate Time Series Anomaly Detectioncited by 0
  19. 2026PhaseNet++: Phase-Aware Frequency-Domain Anomaly Detection for Industrial Control Systems via Phase Coherence Graphscited by 0
  20. 2026No Scale Left Behind: Multi-Scale Autoencoder with Bi-directional Attention for Time Series Anomaly Detectioncited by 0
  21. 2026Detect, Explain, Interpret: An End-to-End Benchmark for Time Series Anomaly Detection, Explainability and Interpretabilitycited by 0
  22. 2026Correlation-Aware Reconstruction for Multivariate Time Series Anomaly Detectioncited by 0