AADS: A Noise-Robust Anomaly Detection Framework for Industrial Control Systems

Author:

Abdelaty Maged,Doriguzzi-Corin Roberto,Siracusa Domenico

Publisher

Springer International Publishing

Reference27 articles.

1. A. Erba, et al.: Real-time evasion attacks with physical constraints on deep learning-based anomaly detectors in industrial control systems. arXiv preprint arXiv:1907.07487 (2019)

2. A. Nichol, et al.: On First-Order Meta-Learning Algorithms. arXiv e-prints arXiv:1803.02999, March 2018

3. Galloway, B., et al.: Introduction to Industrial Control Networks. IEEE Commun. Surv. Tutor. 15(2), 860–880 (2013). https://doi.org/10.1109/SURV.2012.071812.00124

4. Finn, C., et al.: Model-agnostic meta-learning for fast adaptation of deep networks. In: Proceedings of the 34th International Conference on Machine Learning, vol. 70, pp. 1126–1135. JMLR. org (2017)

5. Ahmed, C.M., et al.: Noise matters: using sensor and process noise fingerprint to detect stealthy cyber attacks and authenticate sensors in CPS. In: Proceedings of the 34th Annual Computer Security Applications Conference, pp. 566–581. ACM (2018)

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