Adaptive network security policies via belief aggregation and rollout
File(s)
Published online version
Author(s)
Hammar, Kim
Li, Yuchao
Alpcan, Tansu
Lupu, Emil C
Bertsekas, Dimitri
Type
Journal Article
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 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.
Date Issued
2026-07-01
Date Acceptance
2026-07-01
Citation
IEEE Transactions on Information Forensics and Security, 2026
ISSN
1556-6013
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Journal / Book Title
IEEE Transactions on Information Forensics and Security
Copyright Statement
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
License URL
Publication Status
Published online
Date Publish Online
2026-07-01