Front-End Model of Wireless Network Combined with Artificial Intelligence in Computer Information Management System

Abstract

To address the security problem of computer information management, an artificial intelligence- (AI-) based information intrusion detection model is built in combination with wireless network. Firstly, the background and characteristics of wireless local area network (WLAN) technology are analyzed, and the relationship between AI technology and deep learning is introduced. Secondly, an intrusion detection model on account of long short-term memory (LSTM) neural network and gated recursive unit (GRU) is constructed after analysis of different neural network models. The L2 weight attenuation and dropout regularization strategies are combined with the neural network model. Finally, an intrusion detection front-end model combining wireless network and AI is established. From the comparison of intrusion detection experiments, the generalization ability of the model can be improved by using L2 weight attenuation and dropout regularization strategies. Nevertheless, the performance improvement is only slight, so the early stop method is adopted instead of the regularization strategy. Compared with the existing classification models, the overall performance of LSTM and GRU models is improved by about 17%. The performance of GRU model is not much different from that of LSTM model, but the amount of computation is reduced. Therefore, GRU model is the optimal choice to construct intrusion detection system. The intrusion detection models in WLAN and GRU can improve the security performance of computer information management system. To sum up, this work provides reference for the development of computer management system.