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Adaptive Network Security Policies via Belief Aggregation and Rollout

Control problems in network security are characterized by large combinatorial state spaces, non-stationarity, and partial observability. We present a control framework for such problems. It comprises two main components: offline policy approximation through belief aggregation and online policy improvement through lookahead optimization and rollout. The aggregation enables us to focus computations on the most relevant regions of the belief space, and rollout allows us to adapt control policies to system changes. We prove that the aggregation is asymptotically consistent and bound its error. Experimental results show that our framework is scalable and obtains state-of-the-art performance on the CAGE-2 benchmark, which is a partially observed control problem with over 10^{47} states and 10^{25} observations.