Identifies five attack classes specific to agentic cyber-physical systems and proposes ZTPM with 25 typed primitives across five domains plus Physical Impact Tiers, motivated by 60-trace evidence of model-dependent non-deterministic actuation.
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4 Pith papers cite this work. Polarity classification is still indexing.
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The paper reframes manufacturing ransomware recovery as an interdependency problem, identifies nine evidence-backed failure modes from a multivocal review, and defines Minimum Viable Factory Recovery as an analytical objective for resuming minimal safe operations.
An unsupervised system-aware framework combines online detection with an LLM-augmented contextual digital twin to deliver real-time, interpretable anomaly diagnosis in industrial control systems.
PASTA-4-PHT is an automated DevSecOps-inspired pipeline that detects vulnerabilities in Personal Health Train code before it processes sensitive health data.
citing papers explorer
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When Agents Control Robots: A Zero Trust Policy Model for Agentic Cyber-Physical Systems
Identifies five attack classes specific to agentic cyber-physical systems and proposes ZTPM with 25 typed primitives across five domains plus Physical Impact Tiers, motivated by 60-trace evidence of model-dependent non-deterministic actuation.
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From Backup Restoration to Minimum Viable Factory Recovery: A Systematization of Ransomware Recovery in Manufacturing Systems
The paper reframes manufacturing ransomware recovery as an interdependency problem, identifies nine evidence-backed failure modes from a multivocal review, and defines Minimum Viable Factory Recovery as an analytical objective for resuming minimal safe operations.
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System-aware contextual digital twin for ICS anomaly diagnosis
An unsupervised system-aware framework combines online detection with an LLM-augmented contextual digital twin to deliver real-time, interpretable anomaly diagnosis in industrial control systems.
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PASTA-4-PHT: A Pipeline for Automated Security and Technical Audits for the Personal Health Train
PASTA-4-PHT is an automated DevSecOps-inspired pipeline that detects vulnerabilities in Personal Health Train code before it processes sensitive health data.