Intrusion Detection Model for Industrial Internet of Things Based on Improved Autoencoder

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...

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Bibliographic Details
Published in:Computational intelligence and neuroscience Vol. 2022; pp. 1 - 8
Main Authors: Zhang, Wumei, Zhang, Yongzhen
Format: Journal Article
Language:English
Published: United States Hindawi 27.05.2022
John Wiley & Sons, Inc
Subjects:
Accuracy
Algorithms
Back propagation
Communications traffic
Deep learning
Industrial applications
Industrial Internet of Things
Internet of Things
Intrusion detection systems
Neural networks
Optimization
Research Design
Sparsity
Iran
ISSN:1687-5265, 1687-5273, 1687-5273
Online Access:Get full text
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Summary: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.
Bibliography:ObjectType-Article-1
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Academic Editor: Le Sun
ISSN:1687-5265
1687-5273
1687-5273
DOI:10.1155/2022/1406214