Zhejiang Tongji Vocational College of Science and Technology, HangZhou, Zhejiang 311231, China.
Comput Intell Neurosci. 2022 May 27;2022:1406214. doi: 10.1155/2022/1406214. eCollection 2022.
With the gradual advancement of informatization and industrialization, the safety and controllability of industrial Internet of things (IoT) have attracted more and more attention. Aiming to improve the security of industrial IoT, a detection method using stacked sparse autoencoder network model is proposed. In this method, the basic units of the network model have been simplified and sparse, and some of basic features are combined with obtaining a higher-level abstract expression, so as to solve the problem of unbalanced network traffic data. The cascaded network structure is adopted to stack its sparse autoencoder network model, so as to improve the data ability of the detection model. In addition, the incorporation of Softmax classifier realizes the dynamic adjustment and optimization of the whole network parameters, which further ensures the efficiency of the detection method. The simulation experiment is based on NSL-KDD dataset. The experiment has proved that the proposed method has excellent network attack identification and detection performance. Its accuracy index is about 95.42%, and the detection time is about 3.42 s.
随着信息化和工业化的逐步推进，工业物联网（IoT）的安全性和可控性越来越受到关注。针对提高工业物联网的安全性，提出了一种使用堆叠稀疏自动编码器网络模型的检测方法。在该方法中，网络模型的基本单元被简化和稀疏化，并且一些基本特征与获得更高层次的抽象表达相结合，从而解决了网络流量数据不平衡的问题。采用级联网络结构堆叠其稀疏自动编码器网络模型，从而提高检测模型的数据能力。此外，Softmax 分类器的加入实现了整个网络参数的动态调整和优化，进一步保证了检测方法的效率。模拟实验基于 NSL-KDD 数据集。实验证明，所提出的方法具有出色的网络攻击识别和检测性能。其准确率指标约为 95.42%，检测时间约为 3.42s。
