CO-DECYBER: Co-operative Decision Making for Cybersecurity Using Deep Multi-agent Reinforcement Learning

Author:

Cheah Madeline,Stone Jack,Haubrick Peter,Bailey Samuel,Rimmer David,Till Demian,Lacey Matt,Kruczynska Jo,Dorn Mark

Publisher

Springer Nature Switzerland

Reference15 articles.

1. Dhir, N., Hoeltgebaum, H., Adams, N., Briers, M., Burke, A., Jones, P.: Prospective artificial intelligence approaches for active cyber defence (2021). https://arxiv.org/pdf/2104.09981.pdf

2. Vyas, S., Hannay, J., Bolton, A., Burnap, P.P.: Automated cyber defence: a review (2023). arXiv preprint arXiv:2303.04926

3. Bridges, R.A., et al.: Testing SOAR tools in use. Comput. Secur. 129, 103201 (2023)

4. Jhawar, R., Mauw, S., Zakiuddin, I.: Automating cyber defence responses using attack-defence trees and game theory. In: European Conference on Cyber Warfare and Security, p. 163. Academic Conferences International Limited (2016)

5. Kordy, B., Mauw, S., Melissen, M., Schweitzer, P.: Attack–defense trees and two-player binary zero-sum extensive form games are equivalent. In: Alpcan, T., Buttyán, L., Baras, J.S. (eds) Decision and Game Theory for Security. GameSec 2010. Lecture Notes in Computer Science, vol. 6442. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-17197-0_17

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