The application of Internet of Things in intrusion detection model
Zhiyuan Zhang, Xu Li, Chufeng Zhu · 2023
With the continuous development of 5G technology and big data processing technology, the Internet of Things has been applied to every aspect of human life. At the same time, due to the deployment of a large number of intelligent terminal equipment, massive Internet of Things traffic is generated and sent to the cloud platform storage. In the Internet of Things network environment, there are many high-dimensional, complex data characteristics. These characteristics lead to the unsatisfactory application effect of traditional intrusion detection technology in IoT security protection. Therefore, establishing a complete IoT intrusion detection system has become essential to ensuring IoT security. Since deep learning has powerful data processing and feature learning capabilities, this paper uses deep learning technology to build the Internet of Things intrusion detection model to provide the regular operation of the Internet of Things. The main research work and innovation are aimed at the problems that the sparse stack encoder (SSAE) model is challenging to learn practical features and slow convergence due to its low sparsity in dimensionality reduction of high-dimensional data in the Internet of Things. This paper improves the SSAE model and proposes a BR-SSAE model.