The complexity of the cybersecurity landscape necessitates adaptive strategies for asymmetric cyber defense. Hypergame theory, which incorporates different levels of perception and awareness among players¹, offers a promising framework when combined with AI techniques such as deep reinforcement learning (DRL), tree search, graph neural networks (GNNs), and transformers.
DRL trains agents to learn optimal strategies through trial and error interactions with the environment³, enabling them to dynamically adapt based on the attacker's actions and the evolving network state⁴. Tree search algorithms, like Monte Carlo Tree Search (MCTS), enhance the exploration and evaluation of defense strategies in complex hypergame scenarios⁵,⁶. GNNs model and analyze complex network structures and dynamics⁷,⁸, predicting potential attack paths and identifying critical vulnerabilities. Transformers analyze unstructured threat intelligence data⁹,¹⁰, providing insights into attacker intentions and strategies.
Integrating these AI techniques within a hypergame theoretic framework, as we're building at HypergameAI, presents a powerful approach to asymmetric cyber defense, enabling the development of adaptive, context-aware, and robust strategies. As research advances, the combination of hypergame theory and AI-driven techniques will play a crucial role in strengthening organizational resilience against sophisticated and asymmetric cyber threats.
References:
1. Gharesifard, B., & Cortés, J. (2010). Evolution of players' misperceptions in hypergames under perfect observations. IEEE Transactions on Automatic Control, 55(7), 1627-1640.
2. Kantzavelou, I., & Katsikas, S. (2009, September). A game-theoretical approach to offensive/defensive cyber operations. In International Conference on Trust, Privacy and Security in Digital Business (pp. 161-170). Springer, Berlin, Heidelberg.
3. Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction. MIT press.
4. Nguyen, T. T., & Reddi, V. J. (2019). Deep reinforcement learning for cyber security. arXiv preprint arXiv:1906.05799.
5. Browne, C. B., Powley, E., Whitehouse, D., Lucas, S. M., Cowling, P. I., Rohlfshagen, P., ... & Colton, S. (2012). A survey of monte carlo tree search methods. IEEE Transactions on Computational Intelligence and AI in games, 4(1), 1-43.
6. Hart, S., & Mas-Colell, A. (2013). Simple adaptive strategies: from regret-matching to uncoupled dynamics (Vol. 4). World Scientific.
7. Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., ... & Sun, M. (2020). Graph neural networks: A review of methods and applications. AI Open, 1, 57-81.
8. Scarselli, F., Gori, M., Tsoi, A. C., Hagenbuchner, M., & Monfardini, G. (2008). The graph neural network model. IEEE transactions on neural networks, 20(1), 61-80.
9. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems, 30.
10. Liao, X., Yuan, K., Wang, X., Li, Z., Xing, L., & Beyah, R. (2016, October). Acing the IOC game: Toward automatic discovery and analysis of open-source cyber threat intelligence. In Proceedings of the 2016 ACM SIGSAC conference on computer and communications security (pp. 755-766).