With the rapid development of the times, my country has made good innovation and progress in the Internet of Things technology, which has brought a lot of technical guarantees for computer network security. In the process of computer network security analysis and research, it is necessary to introduce the Internet of Things technology. In this paper, the CNN-GRU-PSO network is used to optimize the information classification control. Compared with the traditional model method, the CNN-GRU-PSO model method improves the accuracy rate from the original 86.4% to the original 95.5%. Nearly 10%; the precision rate increased from 84.3% to the original 91.2%, and the accuracy rate increased to nearly 7%; the recall rate was increased from 86.4% to the original 93.5%, and the accuracy rate increased to nearly 9%. The CNNGRU-PSO model optimizes network security management and formulates strict prevention mechanisms to ensure that computer networks can operate efficiently and safely.
The Internet of Things, as an important part of important data aggregation, forwarding and control, often leads to objectivity errors due to the huge and complex received data. Based on this, this paper introduces GRU, LSTM, SRU deep learning to optimize the data received by the Internet of Things, and selects the most suitable communication mode optimization algorithm. The experimental results show that the accuracy errors of GRU, LSTM, and SRU algorithms show a downward trend, from 0.024 to 0.010%; the training time is reduced by 254 minutes, and the training speed is increased to 86%, indicating the excellent performance of SRU deep learning in IoT gateways.
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