Adaptive Network Security Policies via Belief Aggregation and Rollout

TL;DR

The paper addresses the challenge of adapting network security policies under uncertainty and changing conditions by formulating policy adaptation as a partially observable decision problem. It introduces a scalable three-component framework: belief estimation via particle filtering, offline base policy computation through feature-based belief aggregation, and online policy adaptation via rollout with lookahead. The authors establish a bound on aggregation error, prove rollout-based adaptation improves policy quality under general conditions, and demonstrate state-of-the-art performance on the cage-2 benchmark as well as practical viability in a testbed. The approach offers fast, provable adaptive security with broad applicability to partially observable dynamic systems beyond security. Overall, the work provides a principled, scalable path to reliable automatic policy adaptation in responsive security infrastructures.

Abstract

Evolving security vulnerabilities and shifting operational conditions require frequent updates to network security policies. These updates include adjustments to incident response procedures and modifications to access controls, among others. Reinforcement learning methods have been proposed for automating such policy adaptations, but most of the methods in the research literature lack performance guarantees and adapt slowly to changes. In this paper, we address these limitations and present a method for computing security policies that is scalable, offers theoretical guarantees, and adapts quickly to changes. It assumes a model or simulator of the system and comprises three components: belief estimation through particle filtering, offline policy computation through aggregation, and online policy adaptation through rollout. Central to our method is a new feature-based aggregation technique, which improves scalability and flexibility. We analyze the approximation error of aggregation and show that rollout efficiently adapts policies to changes under certain conditions. Simulations and testbed results demonstrate that our method outperforms state-of-the-art methods on several benchmarks, including CAGE-2.

Figures (18)

• Figure 1: Our method for computing adaptive network security policies. A base policy and cost function are computed offline via dynamic programming in an aggregate belief space, where beliefs represent uncertainty about the system's security state. At runtime, the belief is estimated via particle filtering and the base policy is adapted via rollout simulations and lookahead optimization guided by the cost function. This lookahead allows the system to anticipate possible threats and assess the impact of various security controls.
• Figure 2: Frequency of change in networked systems devops_trends.
• Figure 3: Most common causes of outages in networked systems observability_trends.
• Figure 4: Architecture of the networked system in the example use case.
• Figure 5: Feature-based belief aggregation: we map the state space $X$ into a feature space $\mathcal{F}$, over which beliefs are aggregated via discretization. In this illustration, a subset of states is mapped to a feature space with $4$ elements, where $I_y$ denotes the set of states that aggregate to feature state $y\in \mathcal{F}$. The resulting feature belief space $Q$ is the 3-dimensional unit-simplex.

Theorems & Definitions (3)

• Proposition 1: Approximation error bound
• Proposition 2: Asymptotic optimality
• Proposition 3: Policy improvement of the adaptation