Early warning algorithm for highly sensitive network information based on deep neural network

Abstract

In order to solve the problem that the traditional sensitive information early warning algorithm is difficult to cluster information effectively, a new sensitive information early warning algorithm is proposed. Firstly, the sensitive information of high word frequency in the network was collected. Secondly, the accurate mining of high sensitive information was completed by setting a reasonable threshold. Thirdly, the high sensitive information was effectively clustered by deep neural network, and the adjacency matrix was constructed. Finally, the connection average of network parameter nodes was calculated. Combined with the above clustering results, the monitoring and early warning of high sensitive information were realized. The experimental results showed that the data sent by the network nodes using the traditional algorithm was not regular, and the warning response time was more than 2 s, while the network data using the deep neural network algorithm presents a fully connected state, and the sensitive information could show a high degree of consistency, and the warning response of the algorithm to the sensitive information could be completed in 1 s. The research shows that the early warning algorithm based on deep neural network can be applied in the field of network security.

Keywords

• deep neural network
• highly sensitive information
• network early warning
• data clustering
• adjacency matrix
• average degree of node connection
• early warning and response