One of the major trends in research on Intrusion Response Systems is to use a model of the system to be protected and/or a model of the attacker to predict the evolution of the system and of the strategy of the attacker. However, very often, modeled systems exhibit a non-stationary behavior due to changes in their configuration, in the software base and in the users behavior. If not properly captured by the system model, such a non-stationary behavior could lead to divergences between the expected and the actual behaviors, thus invalidating the model-based approach. In this paper, we introduce a model-free technique for self-defense of non-stationary systems based on Q-Learning. We experimentally show that the proposed approach is able to effectively capture the dynamics of the underlying system and quickly adapts to changes in the environment.
Iannucci, S., Montemaggio, A., & Williams, B. (2019). Towards Self-Defense of Non-Stationary Systems. In 2019 International Conference on Computing, Networking and Communications, ICNC 2019 (pp.250-254). Institute of Electrical and Electronics Engineers Inc. [10.1109/ICCNC.2019.8685487].
Titolo: | Towards Self-Defense of Non-Stationary Systems | |
Autori: | ||
Data di pubblicazione: | 2019 | |
Citazione: | Iannucci, S., Montemaggio, A., & Williams, B. (2019). Towards Self-Defense of Non-Stationary Systems. In 2019 International Conference on Computing, Networking and Communications, ICNC 2019 (pp.250-254). Institute of Electrical and Electronics Engineers Inc. [10.1109/ICCNC.2019.8685487]. | |
Handle: | http://hdl.handle.net/11590/404605 | |
ISBN: | 978-1-5386-9223-3 | |
Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |