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<p>The healthcare cloud ecosystem is getting vulnerable to cyberattacks, zero-day attacks and being plagued by AI-driven attacks, hence the need to use operationally feasible, solid, scientific in background security architectures. The authors of this paper introduce a Multi-Layered Cryptographic Trust Reinforcement (MCTR) framework, which unites hierarchical quantum-resistant cryptography, decentralized trust validation through blockchain and AI-based anomaly detector into an adaptive and coordinated security framework. In contrast to the current hybrid healthcare security models, the offered solution provides mathematically controlled trust development, coordinated multi-node zero-day mitigation, and blockchain-controlled forensic transparency. Large-scale simulations with real-world healthcare data show that the threat detection rate is 95–98%, the false positive rate does not surpass 2.5%, blockchain throughput is over 130 transactions per second, and blockchain is over 91% effective against zero-day attacks with a reasonable level of latency. These results prove that MCTR is technically sound, cannot be computationally unfeasible, and practically can be used in real-time healthcare settings.</p>

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A multi-layered cryptographic trust reinforcement model against AI-driven threat propagation and zero-day cloud vulnerabilities in healthcare data ecosystems

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月3日

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  • Meena Rani,
  • R. Lavanya,
  • K. V. Shahnaz,
  • K. Ramu,
  • Rohit Pachlor,
  • Shitanshu Jain

摘要

The healthcare cloud ecosystem is getting vulnerable to cyberattacks, zero-day attacks and being plagued by AI-driven attacks, hence the need to use operationally feasible, solid, scientific in background security architectures. The authors of this paper introduce a Multi-Layered Cryptographic Trust Reinforcement (MCTR) framework, which unites hierarchical quantum-resistant cryptography, decentralized trust validation through blockchain and AI-based anomaly detector into an adaptive and coordinated security framework. In contrast to the current hybrid healthcare security models, the offered solution provides mathematically controlled trust development, coordinated multi-node zero-day mitigation, and blockchain-controlled forensic transparency. Large-scale simulations with real-world healthcare data show that the threat detection rate is 95–98%, the false positive rate does not surpass 2.5%, blockchain throughput is over 130 transactions per second, and blockchain is over 91% effective against zero-day attacks with a reasonable level of latency. These results prove that MCTR is technically sound, cannot be computationally unfeasible, and practically can be used in real-time healthcare settings.