Metraipolis › archive › dc_dd64f2bfaf83

A Hidden Markov Preference Model for Online Prediction of Adversary Actions — arXiv:2611.08445

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.08445
License (as archived): CC0-1.0
Archived: 2026-11-06T14:51:01.472Z
SHA-256 of the archived text: 6dbb8b1a01ca87eeb08b9ff602cd812822feac79aa31ad8e3a1a19eba3aae447
Stable URL of this page: https://metraipolis.com/documento/dc_dd64f2bfaf83

How to cite

A Hidden Markov Preference Model for Online Prediction of Adversary Actions — arXiv:2611.08445 — arXiv (en), https://arxiv.org/abs/2611.08445 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_dd64f2bfaf83 (Metraipolis archive dc_dd64f2bfaf83, sha256 6dbb8b1a01ca87eeb08b9ff602cd812822feac79aa31ad8e3a1a19eba3aae447)

BibTeX

@misc{dcdd64f2bfaf83,
  title = {A Hidden Markov Preference Model for Online Prediction of Adversary Actions — arXiv:2611.08445},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.08445},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_dd64f2bfaf83; sha256 6dbb8b1a01ca87eeb08b9ff602cd812822feac79aa31ad8e3a1a19eba3aae447},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.08445. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

A defender anticipating an attacker needs a probability distribution over the attacker's next action from information available before that action occurs. Two structures complicate this: the attacker's behavioral regime persists and can switch within an episode, and each action combines an operation with state-dependent arguments whose feasible values change over time. We capture both with a hidden Markov model whose latent state is the active regime and whose regime-conditioned action law is defined on a feasibility-masked tree of executable actions. The same likelihood forms the next-action distribution and updates the regime belief once the action is seen, and a partially observed prefix contributes its resolved local factors. Estimation separates into convex logistic fits that Baum-Welch reuses when regimes are unlabeled. We derive a reach-weighted nodewise KL expression for regime information, a sequence-wise log-loss bound for switching regime paths, and a uniform fallback guarantee. On the CAGE 2 cyber defense benchmark the filtered latent state is worth 0.8 nats per step, and after a controller switch the loss begins to fall in three steps against ten for a recurrent baseline.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.LG, nuovi lavori del giorno).
Categorie arXiv: Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2611.08445 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-06T14:21:42.650Z dalla pagina https://arxiv.org/list/cs.LG/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).