316 – Modern Statistics and Policy in Defense
Probabilistic Graphical Model for Cyber Defensive Policy Assessment and Facilitation of Intuitive Optimal Policy Selection
Pranab Banerjee
Boston Fusion Corp.
Thomas Allen
Boston Fusion Corp.
The age of cyber warfare necessitates effective defensive plans for operational integrity of networked security assets. Under a cyber attack, a decision maker needs to select the most effective defensive action (policy) from a set of feasible policies brought forth by domain experts and/or automated policy generators. However, selecting an optimal policy is non-trivial in practice because of complex dependencies among constituent components of a critical operational system; temporally dynamic mission goals; and uncertain knowledge about the states of some components. To address these issues, a Bayesian network based probabilistic framework was developed to assess the impact of a policy on mission success. At the core is a probabilistic graphical mission model built on top of the assets terrain based on domain knowledge. The framework quantifies the probability of mission success under a policy as a score, and intuitively explains the propagation of policy effects leading to the mission outcome, thus facilitating optimal policy selection. For a mission composed of temporally ordered sub-tasks, the Bayesian network is dynamically pruned based on currently completed steps.