A multi-layer framework combining POMDP-level strategic analysis and policy-level Q-value/PER tracking to explain RL-based cyber attacker behavior in simulated environments.
Inroads into autonomous network defence using explained reinforcement learning,
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2025 2verdicts
UNVERDICTED 2representative citing papers
Combines particle filtering, feature-based aggregation, and rollout to produce scalable network security policies with theoretical guarantees that adapt quickly to model changes.
citing papers explorer
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Unveiling the Black Box: A Multi-Layer Framework for Explaining Reinforcement Learning-Based Cyber Agents
A multi-layer framework combining POMDP-level strategic analysis and policy-level Q-value/PER tracking to explain RL-based cyber attacker behavior in simulated environments.
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Adaptive Network Security Policies via Belief Aggregation and Rollout
Combines particle filtering, feature-based aggregation, and rollout to produce scalable network security policies with theoretical guarantees that adapt quickly to model changes.