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Clinical-Protocol-Aware Cyber Resilience for Connected Clinical Technologies Under AI-Enabled Adversarial Conditions — arXiv:2610.41784

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.41784
License (as archived): CC0-1.0
Archived: 2026-11-03T07:11:39.456Z
SHA-256 of the archived text: 8dbeadc0c48e01531c832cb2bf88fef11b02168baa94fbdd5e6e131b8d935168
Stable URL of this page: https://metraipolis.com/documento/dc_7498faf50eee

How to cite

Clinical-Protocol-Aware Cyber Resilience for Connected Clinical Technologies Under AI-Enabled Adversarial Conditions — arXiv:2610.41784 — arXiv (en), https://arxiv.org/abs/2610.41784 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_7498faf50eee (Metraipolis archive dc_7498faf50eee, sha256 8dbeadc0c48e01531c832cb2bf88fef11b02168baa94fbdd5e6e131b8d935168)

BibTeX

@misc{dc7498faf50eee,
  title = {Clinical-Protocol-Aware Cyber Resilience for Connected Clinical Technologies Under AI-Enabled Adversarial Conditions — arXiv:2610.41784},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.41784},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_7498faf50eee; sha256 8dbeadc0c48e01531c832cb2bf88fef11b02168baa94fbdd5e6e131b8d935168},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.41784. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Healthcare increasingly relies on connected medical devices, clinical information systems, cloud services, and distributed-care technologies, creating cybersecurity risks across clinical workflows. AI-enabled capabilities can accelerate vulnerability discovery and attack-path analysis, increasing the need to limit compromise propagation. This paper proposes a clinical-protocol-aware cyber-resilience framework based on bounded clinical micro-networks and controlled interconnections. The framework integrates clinical factors into cybersecurity design, management, and evaluation, covering prevention, containment, and incident response. A parameterized Monte Carlo simulation distinguishes technical cyber exposure from modeled clinical consequence. Experiments investigate architectural connectivity, adversarial capability, security controls, protocol dependency, risk alignment, communication directionality, lifecycle integration, and response timing. Under the simulation assumptions, connected architecture increases mean cyber exposure by 56.3% relative to segmented architecture, while combined prevention and containment reduce exposure by 60.1% relative to no controls. Increasing protocol dependency raises the modeled clinical-impact index from 1.021 to 7.567 without changing mean cyber exposure, demonstrating that technical exposure and clinical consequence can diverge. Risk-alignment experiments further indicate higher modeled clinical impact when greater technical risk corresponds to greater clinical significance. The findings support comparative architectural analysis, not calibrated estimates of real-world cyber risk, control effectiveness, or patient harm. The framework provides a clinical-context-aware approach to security-boundary design, control prioritization, and coordinated containment in connected healthcare environments.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Artificial Intelligence (cs.AI) ; Computational Engineering, Finance, and Science (cs.CE); Emerging Technologies (cs.ET); Networking and Internet Architecture (cs.NI); Systems and Control (eess.SY).
Pagina del lavoro: https://arxiv.org/abs/2610.41784 — 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 2026-11-03T07:09:31.789Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).