# Adaptive Network Security Policies via Belief Aggregation and Rollout

arXiv: https://arxiv.org/abs/2507.15163 · HTML: https://mlsanity.com/paper/2507.15163/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jul 2025.

## Built on by (most cited first, 6 of 6)

- [Incident Response Planning Using a Lightweight Large Language Model with Reduced Hallucination](https://mlsanity.com/paper/2508.05188/) (2025)
- [Online Incident Response Planning under Model Misspecification through Bayesian Learning and Belief Quantization](https://mlsanity.com/paper/2508.14385/) (2025)
- [CSLE: A Reinforcement Learning Platform for Autonomous Security Management](https://mlsanity.com/paper/2604.15590/) (2026)
- [Hallucination-Resistant Security Planning with a Large Language Model](https://mlsanity.com/paper/2602.05279/) (2026)
- [Online Identification of IT Systems through Active Causal Learning](https://mlsanity.com/paper/2509.02130/) (2025)
- [Optimal Stopping of Self-Refining Foundation Models](https://mlsanity.com/paper/2608.10729/) (2026)

## Abstract (arXiv)

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 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. The method uses a model or simulator of the system, which is updated when changes occur, and combines three components: belief estimation through particle filtering, offline policy computation through feature-based aggregation, and online policy adaptation through rollout. In particular, feature-based aggregation enables scalable offline optimization of a policy, while rollout adapts the policy online to changes in the system model without repeating the offline optimization. We analyze the approximation error of the aggregation and show that the 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.