Empirical Evaluation of Decision Policies for Security Probing under Partial Observability: Efficiency Gains and Entropy-Induced Failure Regimes
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| Format: | Recurso digital |
| Sprache: | Englisch |
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2025
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| _version_ | 1866901866344873984 |
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| author | Pérez Contreras, Benjamín Felipe |
| author_facet | Pérez Contreras, Benjamín Felipe |
| contents | <p>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.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18099840 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Empirical Evaluation of Decision Policies for Security Probing under Partial Observability: Efficiency Gains and Entropy-Induced Failure Regimes Pérez Contreras, Benjamín Felipe Security, Offensive Security, POMDP, Machine Learning, Penetration Testing, Decision Policies <p>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.</p> |
| title | Empirical Evaluation of Decision Policies for Security Probing under Partial Observability: Efficiency Gains and Entropy-Induced Failure Regimes |
| topic | Security, Offensive Security, POMDP, Machine Learning, Penetration Testing, Decision Policies |
| url | https://doi.org/10.5281/zenodo.18099840 |