Research on Communication Network Structure Mining Based on Spectrum Monitoring Data

Abstract

The physical characteristics of the massive spectrum signals carrying the communication information and the statistical laws of these characteristics also potentially reflect the communication behavior of the communication individuals and the intelligence information related to the communication behavior. Intercepting and cracking signal content usually faces enormous difficulties and costs, and more often, we are not able to crack the encrypted signal content. However, by studying the physical features extracted from the spectrum monitoring signals and the statistical laws of these features, it is also possible to dig out the hidden relationships between communication individuals and even the communication network structure, so as to analyze the communication behaviors of the communication individuals. Based on the characteristics of carrier frequency, bandwidth, power, signal monitoring time and direction information of spectrum monitoring signals, this paper identifies each spectrum signal and studies the distribution characteristics and statistical laws of massive spectrum monitoring signals in the column coordinate system. Due to the clustering of the spectrum signals generated by the sources in the power, monitoring time and direction, and the correlation of the spectrum signals generated by the two parties in the communication process, based on the improved density clustering algorithm, this paper proposes a method for mining the communication relationship between communication individuals from the spectrum monitoring data, and guesses and constructs the communication network structure by matching the communication individual with the communication relationship. Finally, we analyze the communication network structure mined from the spectrum monitoring data.INDEX TERMS Spectrum monitoring data, communication network structure, communication relationship discovery, data mining, density clustering.

Identification of Shortwave Radio Communication Behavior Based on Autocorrelation Spectrogram Features

“…Research on communication behavior can be divided into communication group behavior [22,23] and communication individual behavior [24][25][26][27][28][29][30][31][32][33][34][35][36]. The former mainly refers to a series of combat tasks coordinated by groups equipped with CRS, while the latter mainly refers to the working modes, tasks of a certain CRS, and the actions of the person or unit operating the CRS.…”

“…At present, relevant research is mainly based on the following three methods. The first method is data mining for spectrum monitoring data to gain communication relationships [24][25][26][27][28][29]. The second method is to obtain the communication behavior by parsing the communication protocol.…”

“…In the literature [25], DBSCAN is improved and used to mine the hidden communication behavior of spectrum data. By mining the physical characteristics of the spectrum monitoring signal and analyzing their statistical laws, individual communication behavior can be known [26]. Mining of communication relations in the case of missing and disordered data is realized in the literature [27].…”