Federated hierarchical MARL for zero-shot cyber defense.

ABSTRACT: Cyber defense systems face increasingly sophisticated threats that rapidly evolve and exploit vulnerabilities in complex environments. Traditional approaches which often rely on centralized monitoring and static rule-based detection, struggle to adapt to new, crafted, and novel attack patterns. This paper presents the Adaptive Zero-Shot Hierarchical Multi-Agent Reinforcement Learning (AZH-MARL) framework, a novel approach that integrates hierarchical reinforcement learning, zero-shot learning capabilities, and federated knowledge sharing to build resilient cyber defense systems. The hierarchical structure decomposes complex defense tasks into specialized sub-tasks managed by agents, reducing the learning problem's complexity and enabling more efficient coordination. The zero-shot learning

SUBMITTER: Alshamrani A 

PROVIDER: S-EPMC12334051 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature