发表机构
Unitec Institute of Technology; Federation University Australia（Unitec理工学院; 联邦大学澳大利亚）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出基于Dempster-Shafer理论融合数据关键性与安全漏洞的框架，量化IIoT传感器节点关键性，并在红酒生产数据集上验证，发现CVSS 4.0与3.1排名差异显著。
AI 中文摘要
工业物联网（IIoT）融入制造业，通过基于实时数据调节过程的智能工业传感器优化生产管理并确保产品质量，从而变革了工业运营。然而，这些传感器节点极易受到网络威胁，构成重大安全风险，损害其可靠性和完整性。虽然现有研究探索了网络安全漏洞和基于网络攻击的关键节点排序方法，一些研究则根据传感器数据对产品质量的影响来评估节点关键性。然而，一种同时整合数据关键性和网络安全漏洞的综合方法仍未得到探索。为弥补这一空白，本研究提出了一种新颖框架，利用Dempster-Shafer（D-S）理论融合数据关键性和网络安全漏洞来评估IIoT传感器节点关键性。所提方法使用红葡萄酒生产数据集进行验证，展示了其基于这两个因素对传感器节点进行排序的有效性。结果表明，基于CVSS 4.0版本计算的安全漏洞得分所得的关键性排名与使用CVSS 3.1版本所得排名存在显著差异，凸显了增强漏洞评估方法的影响。虽然最初应用于葡萄酒制造，该框架只需最小修改即可适应更广泛的工业应用，为保护启用IIoT的生产系统提供了一种稳健方法。
英文摘要
The integration of the Industrial Internet of Things (IIoT) into manufacturing has transformed industrial operations by optimising production management and ensuring product quality through smart industrial sensors that regulate processes based on real-time data. However, these sensor nodes are highly vulnerable to cyber threats, posing significant security risks that compromise their reliability and integrity. While existing research explores cybersecurity vulnerabilities and cyberattack-based methods for ranking critical nodes, some studies assess node criticality based on the impact of sensor data on product quality. However, a comprehensive approach that integrates both data criticality and cybersecurity vulnerability remains unexplored. To bridge this gap, this study introduces a novel framework that evaluates IIoT sensor node criticality by leveraging Dempster--Shafer (D-S) theory to fuse data criticality and cybersecurity vulnerabilities. The proposed method is validated using a dataset from red wine production, demonstrating its effectiveness in ranking sensor nodes based on both factors. The results show that criticality rankings based on security vulnerability scores computed using CVSS version 4.0 differ significantly from those obtained with CVSS version 3.1, highlighting the influence of enhanced vulnerability assessment methodologies. While initially applied to wine manufacturing, this framework is adaptable to broader industrial applications with minimal modifications, offering a robust approach to securing IIoT-enabled production systems.
Comments16 pages, 4 Figures
