Tactical communication network key node identification method based on physical layer data

Abstract

The invention discloses a tactical communication network key node identification method based on physical layer data, and relates to the technical field of network communication. The method comprisesthe steps of collecting physical layer data from a tactical communication network, training a full-connection BP neural network, acquiring a simulation time sequence signal of a MAC protocol, extracting a characteristic parameter set I consisting of frame analogy parameters and duty ratio parameters in the simulation time sequence signal, training a support vector machine, extracting a characteristic parameter set II composed of frame analogy parameters and duty ratio parameters in the networking data, identifying the MAC protocol used by the tactical communication network, extracting a time sequence segment to form sample data II, marking a network node corresponding to each data segment, performing mode matching on the networking data, identifying a topological structure of the tacticalcommunication network, and according to the topological structure of the tactical communication network, identifying key network nodes of the tactical communication network. According to the method, the network topology structure of the tactical communication network can be obtained without performing protocol analysis on physical layer data, and the key network nodes can be identified according to the network topology structure.

Description

technical field [0001] The invention relates to the technical field of network communication, in particular to a method for identifying key nodes of a tactical communication network based on physical layer data. Background technique [0002] In recent years, the identification methods of key network nodes are mainly divided into two categories: the first category is social network analysis method, including: degree value method, betweenness method, k-shell decomposition method, eigenvector method, etc. The research of indicators does not destroy the integrity of the network; the second type is the node deletion method, which is the destructiveness of the network after deleting the key equivalent nodes of the nodes, and the research of indicators destroys the network. integrity. With the rise of neural networks, more and more people use intelligent means for data mining to identify key nodes, but these studies are graph or graph-based mining, and a small number of special ap...

AI Technical Summary

Problems solved by technology

However, the most notable feature of the tactical communication network is its high confidentiality and high reliability. It is difficult to obtain the topological structure without analyzing

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