用软件定义孪生网络实现工业物联网短期异常预测
A Novel Short-Term Anomaly Prediction for IIoT with Software Defined Twin Network
- 结合SDN与数字孪生构建实时监控框架
- 基于时间特征标注的LightGBM模型召回率高且分类精准
- 适合工业物联网安全团队部署使用
工业物联网（IIoT）环境中的安全监控与动态控制是当前发展的关键需求。本文提出一种基于软件定义网络（SDN）的数字孪生（DT）框架，实现对IIoT环境的动态安全监测。针对现有研究中缺乏基于SDN的数字孪生实现细节及面向短时异常检测的时间感知智能模型训练问题，本文设计了一种新型的SD-TWIN异常检测算法。通过综合数据集、时间感知的特征标注方法，并对多种机器学习模型进行系统评估，实验表明在真实场景下的实时部署中，采用GPU加速的LightGBM模型表现出优异性能，在保持高召回率的同时具备强分类能力。
原文摘要 · Abstract (English)
Secure monitoring and dynamic control in an IIoT environment are major requirements for current development goals. We believe that dynamic, secure monitoring of the IIoT environment can be achieved through integration with the Software-Defined Network (SDN) and Digital Twin (DT) paradigms. The current literature lacks implementation details for SDN-based DT and time-aware intelligent model training for short-term anomaly detection against IIoT threats. Therefore, we have proposed a novel framework for short-term anomaly detection that uses an SDN-based DT. Using a comprehensive dataset, time-aware labeling of features, and a comprehensive evaluation of various machine learning models, we propose a novel SD-TWIN-based anomaly detection algorithm. According to the performance of a new real-time SD-TWIN deployment, the GPU- accelerated LightGBM model is particularly effective, achieving a balance of high recall and strong classification performance.
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