Security Analysis of Social Network Topic Mining Using Big Data and Optimized Deep Convolutional Neural Network

Abstract

This research aims to conduct topic mining and data analysis of social network security using social network big data. At present, the main problem is that users' behavior on social networks may reveal their private data. The main contribution lies in the establishment of a network security topic detection model combining Convolutional Neural Network (CNN) and social network big data technology. Deep Convolution Neural Network (DCNN) is utilized to complete the analysis and search of social network security issues. The Long Short-Term Memory (LSTM) algorithm is used for the extraction of Weibo topic information in the memory wisdom. Experimental results show that the recognition accuracy of the constructed model can reach 96.17% after 120 iterations, which is at least 5.4% higher than other models. Additionally, the accuracy, recall, and F1 value of the intrusion detection model are 88.57%, 75.22%, and 72.05%, respectively. Compared with other algorithms, the model's accuracy, recall, and F1 value are at least 3.1% higher than other models. In addition, the training time and testing time of the improved DCNN network security detection model are stabilized at 65.86 s and 27.90 s, respectively. The prediction time of the improved DCNN network security detection model is significantly shortened compared with that of the models proposed by other scholars. The experimental conclusion is that the improved DCNN has the characteristics of lower delay under deep learning. The model shows good performance for network data security transmission.