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A Hybrid Deep Learning Framework for Enhanced Anomaly Detection in High-Dimensional Industrial Control Systems

Authors: Narendra Kumar

Published: 2025-05-02 11:45:40.106

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Abstract

Industrial Control Systems (ICS) are increasingly vulnerable to sophisticated cyberattacks, necessitating robust anomaly detection mechanisms. This paper proposes a novel hybrid deep learning framework for enhanced anomaly detection in high-dimensional ICS data. The framework combines the strengths of Autoencoders (AEs) for feature extraction and dimensionality reduction with Long Short-Term Memory (LSTM) networks for temporal sequence modeling. The AE first learns a compressed representation of normal ICS operational data, effectively capturing the underlying system dynamics. The LSTM network then models the temporal dependencies within the reduced feature space. Anomalies are detected by identifying deviations from the learned normal behavior, leveraging both the reconstruction error of the AE and the prediction error of the LSTM. We evaluate the proposed framework on a benchmark ICS dataset, demonstrating its superior performance compared to state-of-the-art anomaly detection methods in terms of detection accuracy, false positive rate, and robustness to noise. The results highlight the potential of the hybrid approach to significantly improve the security and reliability of critical industrial infrastructure.

Keywords

Anomaly Detection, Industrial Control Systems (ICS), Deep Learning, Hybrid Model, Autoencoders, LSTM, Cybersecurity, Feature Extraction, Threat Detection, Multivariate Time Series

References

  2. Eskin, E., Arnold, S., Prerau, M., Portnoy, L., & Stolfo, S. (2002). A geometric framework for unsupervised anomaly detection: Detecting intrusions in unlabeled data. Applications of Data Mining in Computer Security, 1-16.
  3. Hyttinen, M., & Kortela, E. (2004). Process monitoring using dynamic time warping. Control Engineering Practice, 12(1), 75-82.
  4. Ryan, C. G., & den Hartog, J. (1998). Supervised machine learning applied to intrusion detection. Proceedings of the IEEE International Carnahan Conference on Security Technology, 69-74.
  5. Mukkamala, S., Sung, A. H., & Abraham, A. (2005). Intrusion detection using ensemble of classifiers. International Journal of Computational Intelligence and Applications, 5(02), 135-146.
  6. Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM Computing Surveys (CSUR), 41(3), 1-58.
  7. Tax, D. M. J. (2001). One-class classification. Delft University of Technology.
  8. Sakurada, M., & Yairi, T. (2014). Anomaly detection using autoencoders with nonlinear dimensionality reduction. Proceedings of the 2014 International Conference on Machine Learning and Applications, 90-95.
  9. Malhotra, P., Ramakrishnan, A., Anand, G., Vig, L., Agarwal, P., & Shroff, G. (2016). Long short term memory networks for anomaly detection in time series. Proceedings of the 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, 89-94.
  10. Yemini, E., & Kushilevitz, E. (2010). Anomaly detection in industrial processes using self-organizing maps and support vector machines. Engineering Applications of Artificial Intelligence, 23(6), 1005-1013.
  11. Zhang, X., Zhu, Y., & Cheng, L. (2012). Anomaly detection in industrial control systems based on PCA and KNN. Proceedings of the International Conference on Information Technology and Management Science, 825-831.
  12. Audibert, J. Y., Michiardi, P., Molinari, P., & Filippone, M. (2020). Usad: Unsupervised anomaly detection on multivariate time series. Knowledge and Information Systems, 62(9), 3177-3202.
  13. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT press.
  14. Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780.
  15. Kingma, D. P., & Welling, M. (2013). Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114.
  16. Goh, J., Adepu, S., Tan, S. C., & Mathur, A. P. (2016). A dataset to support research in the design of secure water treatment systems. Proceedings of the 8th International Symposium on Industrial Control Systems and Security*, 74-83.
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How to Cite

Narendra Kumar, (2025-05-02 11:45:40.106). A Hybrid Deep Learning Framework for Enhanced Anomaly Detection in High-Dimensional Industrial Control Systems. JANOLI International Journal of Artificial Intelligence and its Applications, Volume EOCMPeqBj5R9ZDur0Rlk, Issue 4.

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