Enhancing Critical Infrastructure Security: Unsupervised Learning Approaches for Anomaly Detection
Author:
Funder
Universidad de los Andes
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s44196-024-00644-z.pdf
Reference29 articles.
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2. Nazir, S., Patel, S., Patel, D.: Autoencoder based anomaly detection for SCADA networks. Int. J. Artif. Intell. Mach. Learning 11(2), 83–99 (2021). https://doi.org/10.4018/ijaiml.20210701.oa6
3. Boateng, E.A., Bruce, J.W., Talbert, D.A.: Anomaly detection for a water treatment system based on one-class neural network. IEEE Access 10, 115179–115191 (2022). https://doi.org/10.1109/ACCESS.2022.3218624
4. Raman, M.R.G., Somu, N., Mathur, A.P.: A multilayer perceptron model for anomaly detection in water treatment plants. Int J. Crit. Infrastruct. Protect. (2020). https://doi.org/10.1016/j.ijcip.2020.100393
5. Nedeljkovic, D., Jakovljevic, Z.: CNN based method for the development of cyber-attacks detection algorithms in industrial control systems. Comput. Secur. (2022). https://doi.org/10.1016/j.cose.2021.102585
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