Ponnoly, J., Puthenveetil, J., & D’Urso, P. (2024, February). Prescriptive Analytics-based Robust Decision-Making Model for Cyber Disaster Risk Reduction. In 2024 IEEE 3rd International Conference on AI in Cybersecurity (ICAIC) (pp. 1-5). IEEE.
https://www.researchgate.net/publication/378271477_Prescriptive_Analytics-based_Robust_Decision-Making_Model_for_Cyber_Disaster_Risk_Reduction
Decision-making in cyber security attack scenarios involves deep uncertainty and adversarial decision-making. Robust Decision Making (RDM) uses a structured approach to evaluate the performance of various decision strategies under conditions of deep uncertainty to enable adaptive decision-making. The challenge is in getting reliable inputs to the RDM model. It is suggested that prescriptive analytics enabled by predictive analytics including big data analytics, reinforcement learning, and Monte Carlo simulations, could provide decision-makers with various options to make informed and robust decision-making. Prescriptive analytics based RDM model is proposed for cyber disaster risk reduction. The model extends the proactive cyber defense model based on sensing and sensemaking of early warning signs of cyber disasters.