Model Construction and Analysis of Deep Learning-based Cybersecurity Awareness Enhancement for College Students

Abstract

This paper constructs a network security intelligence analysis model based on deep learning methods. Firstly, the weights and thresholds of network packets are modeled using the convolutional neural network algorithm to extract the main information features. Then, the backward propagation algorithm is used for layer-by-layer propagation, combined with an unsupervised autoencoder to achieve the network parameter update. The results show that the model can recognize a variety of network viruses, with an average detection rate of 97%, and the error rate is kept around 0.5%. The network security intelligence analysis model is based on the deep learning method to analyze and warn about network intrusion data, effectively improving college students’ awareness about network security.

Keywords

• deep learning
• cybersecurity
• convolutional neural network
• backward propagation
• self-encoder
• 97q70