Recognition of Connection Relationship Based on the Spectrum Monitoring Data by Deep Convolutional Neural Networks
Haibo Zhang, Lei Zhu, Changhua Yao, Lei Wang, Huaqiao Zhou, Fanpeng Zhu · 2020
The connection relationship can reflect the hidden information in the network to a certain extent. Through the recognition of the connection relationship, it can infer its hierarchical position in the communication network, and then find the key nodes in the network and analyze the hidden information in the network. To overcome the obstacles of analyzing communication information from frequency hopping signal cracking, a method for recognition of connection relationships based on deep neural networks is proposed by this paper. Automatically learn features directly from simple spectrum monitoring data without the need to manually extract expert features. This paper implements the solution to the problem of imbalanced data categories of self-built data sets; proposes two design schemes based on different design goals and analyzes the performance of these two design schemes; attempts to use Convolutional Neural Networks (CNN) with different input lengths to realize the recognition, it is found that the input length affects the recognition result; the output of the neural network is corrected by the physical meaning so that the recognition result is better. The experimental results show that the CNN models are suitable for connection relationship recognition.