增强大数据无线医疗网络中的数字取证就绪性：一个安全的去中心化框架
Enhancing Digital Forensics Readiness In Big Data Wireless Medical Networks: A Secure Decentralised Framework
AI总结：
本文针对大数据无线医疗网络中的数字取证挑战，提出一个安全的去中心化框架，以增强数字取证就绪性、事件响应能力和医疗事故管理，并通过真实场景评估验证其在大数据处理、去中心化存储和可扩展性方面的优势。
摘要：
无线医疗网络对于慢性病管理至关重要，然而其产生的敏感大数据带来了管理挑战和网络脆弱性。这些大数据在医疗和法律领域都具有重要价值，可作为调查医疗事故、民事案件、犯罪活动以及网络相关事件的资源。然而，网络技术和数据的快速演进给数字取证调查和审计带来了复杂性。为解决这些问题，本文提出了一个安全的去中心化框架，旨在通过识别安全威胁、复杂性以及当前研究工作中的不足，来增强大数据无线医疗网络中的数字取证就绪性（DFR）。通过提高网络对网络威胁的抵御能力并协助医疗事故调查，该框架显著推动了数字取证、无线网络和医疗保健领域的发展。它增强了大数据的数字取证就绪性、事件响应能力以及医疗事故事件管理。基于真实场景的评估表明，该框架在提高取证就绪性和响应能力方面具有有效性，验证了其实际适用性和影响力。将所提出的框架与现有框架进行比较后得出结论：该框架在DFR框架设计方面是一项进步，尤其是在大数据处理、去中心化DFR存储和可扩展性方面。
英文摘要：
Wireless medical networks are pivotal for chronic disease management, yet the sensitive Big Data they generate presents administration challenges and cyber vulnerability. This Big Data is valuable within both healthcare and legal contexts, serving as a resource for investigating medical malpractice, civil cases, criminal activities, and network-related incidents. However, the rapid evolution of network technologies and data creates complexities in digital forensics investigations and audits. To address these issues, this paper proposes a secure decentralised framework aimed at bolstering digital forensics readiness (DFR) in Big Data wireless medical networks by identifying security threats, complexities, and gaps in current research efforts. By improving the network's resilience to cyber threats and aiding in medical malpractice investigations, this framework significantly advances digital forensics, wireless networks, and healthcare. It enhances digital forensics readiness, incident response, and the management of medical malpractice incidents in Big Data wireless medical networks. A real-world scenario-based evaluation demonstrated the framework's effectiveness in improving forensic readiness and response capabilities, validating its practical applicability and impact. A comparison of the proposed framework with existing frameworks concluded that it is an advancement in framework design for DFR, especially in regard to Big Data processing, decentralised DFR storage and scalability.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
