Empirical Evaluation of Decision Policies for Security Probing under Partial Observability: Efficiency Gains and Entropy-Induced Failure Regimes

This work presents an empirical evaluation of decision policies for security probing under partial observability. We analyze efficiency gains and entropy-induced failure regimes in automated security testing scenarios. Using methods from reinforcement learning and sequential decision-making, we quantify the performance of various probing strategies and highlight trade-offs between exploration and exploitation under uncertainty. Our results provide actionable insights for designing robust autonomous security systems and contribute to the theoretical understanding of decision-making in partially observable stochastic environments.