Research Article
Dynamic Mining of Wireless Network Information Transmission Security Vulnerabilities Based on Spatiotemporal Dimension
@INPROCEEDINGS{10.1007/978-3-031-50546-1_27, author={Qiang Chen and Fang Qian and Yukang Liu}, title={Dynamic Mining of Wireless Network Information Transmission Security Vulnerabilities Based on Spatiotemporal Dimension}, proceedings={Advanced Hybrid Information Processing. 7th EAI International Conference, ADHIP 2023, Harbin, China, September 22-24, 2023, Proceedings, Part II}, proceedings_a={ADHIP PART 2}, year={2024}, month={3}, keywords={Spatiotemporal Dimension Wavelet Transform Deep Neural Network Instruction Level Word Embedding Dynamic Vulnerability Mining}, doi={10.1007/978-3-031-50546-1_27} }- Qiang Chen
Fang Qian
Yukang Liu
Year: 2024
Dynamic Mining of Wireless Network Information Transmission Security Vulnerabilities Based on Spatiotemporal Dimension
ADHIP PART 2
Springer
DOI: 10.1007/978-3-031-50546-1_27
Abstract
In order to improve the efficiency of dynamic mining for wireless network information transmission security vulnerabilities and improve the accuracy of mining results, this paper proposes a dynamic mining method for wireless network information transmission security vulnerabilities based on the spatiotemporal dimension. Firstly, collect data on security vulnerabilities in wireless network data transmission; Secondly, wavelet transform is introduced to filter and process wireless network information transmission security vulnerability data; Then, in the deep neural network architecture, the instruction level word embedding method based on Word2vec obtains the feature attributes of wireless network information transmission security vulnerabilities; Finally, dynamically mine wireless network information transmission security vulnerabilities based on the spatiotemporal dimension. The experimental results show that the vulnerability dynamic mining method proposed in this paper takes 25.8 s, with an accuracy of 99.0% and a recall rate of 98.1%, which can improve the effectiveness of vulnerability dynamic mining.
