Efficiency at Risk: Entropy-Induced Failure in Learned Decision Policies under Partial Observability
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| Natura: | Recurso digital |
| Lingua: | inglese |
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Zenodo
2026
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| _version_ | 1866901868632866816 |
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| author | Pérez Contreras, Benjamín Felipe |
| author_facet | Pérez Contreras, Benjamín Felipe |
| contents | <p>This work presents an empirical evaluation of automated security probing policies operating under partial observability. We quantify efficiency gains achieved by learned decision models and identify entropy-induced failure regimes that emerge under increasing observational noise. By modeling the interaction as a Partially Observable Markov Decision Process (POMDP), we demonstrate that while learned policies significantly reduce average Time-to-Compromise, they exhibit heavy-tailed operational risk characterized by instability, action thrashing, and elevated detection likelihood. We further propose a hybrid meta-control architecture that bounds worst-case behavior by supervising learned policies with entropy-aware fallback mechanisms. Our results highlight a fundamental efficiency–stability trade-off and provide actionable guidance for the safe deployment of autonomous security decision systems in stochastic environments. - </p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19058748 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Efficiency at Risk: Entropy-Induced Failure in Learned Decision Policies under Partial Observability Pérez Contreras, Benjamín Felipe Security, Offensive Security, POMDP, Machine Learning, Penetration Testing, Decision Policies <p>This work presents an empirical evaluation of automated security probing policies operating under partial observability. We quantify efficiency gains achieved by learned decision models and identify entropy-induced failure regimes that emerge under increasing observational noise. By modeling the interaction as a Partially Observable Markov Decision Process (POMDP), we demonstrate that while learned policies significantly reduce average Time-to-Compromise, they exhibit heavy-tailed operational risk characterized by instability, action thrashing, and elevated detection likelihood. We further propose a hybrid meta-control architecture that bounds worst-case behavior by supervising learned policies with entropy-aware fallback mechanisms. Our results highlight a fundamental efficiency–stability trade-off and provide actionable guidance for the safe deployment of autonomous security decision systems in stochastic environments. - </p> |
| title | Efficiency at Risk: Entropy-Induced Failure in Learned Decision Policies under Partial Observability |
| topic | Security, Offensive Security, POMDP, Machine Learning, Penetration Testing, Decision Policies |
| url | https://doi.org/10.5281/zenodo.19058748 |