Simulating Deception for Web Applications Using Reinforcement Learning
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-54129-2_42
Reference33 articles.
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3. Caminero Fernández, G., Lopez-Martin, M., Carro, B.: Adversarial environment reinforcement learning algorithm for intrusion detection. Comput. Netw. 159, 96–109 (2019). https://doi.org/10.1016/j.comnet.2019.05.013
4. Charpentier, A., Boulahia Cuppens, N., Cuppens, F., Yaich, R.: Deep Reinforcement Learning-Based Defense Strategy Selection. In: Proceedings of the 17th International Conference on Availability, Reliability and Security, pp. 1–11. ACM, Vienna Austria (Aug 2022). https://doi.org/10.1145/3538969.3543789
5. El-Kosairy, A., Azer, M.A.: A New web deception system framework. In: 2018 1st International Conference on Computer Applications & Information Security (ICCAIS), pp. 1–10 (Apr 2018). https://doi.org/10.1109/CAIS.2018.8442027
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