Assurance of Network Communication Information Security Based on Cyber-Physical Fusion and Deep Learning

Abstract

The internet brings high efficiency and convenience to society; however, the issue of information security in network communication has significantly affected every aspect of the society. How to ensure the security of this network communication information has become an important research topic. This paper proposes a diagnosis and prediction method based on cyber-physical fusion and deep learning, such as LSTM and CNN, to diagnose and predict network security in a complex network environment. The experiment results showed that the accuracy of network security diagnosis of the LSTM method in the training set was approximately 80%/ After the CNN training process, it has the highest accuracy rate of 95% on the test data set. This paper analysed the nature of network security problems from the perspective of cyber-physical fusion. CNN-based method to diagnose network security can obtain results with a higher accuracy rate so that technicians can better take measures to protect network security.