Multi-Agent System Network Intrusion Tolerance Evaluation Method Based on Multilayer Perceptron

Abstract

The invention discloses a multi-agent system network intrusion tolerance evaluation method based on a multi-layer perceptron. Traditional methods cannot be effectively evaluated due to the large number of network nodes and high algorithm complexity. The method of the invention firstly conducts matrix spectrum clustering according to the number of nodes for the multi-agent system network set, obtains the distribution number of the eigenvectors of the adjacency matrix in different clusters, and constructs a set of eigenvectors; The features extracted from the data set with the number of nodes are handed over to the multi-layer perceptron model for feature learning, and the weight matrix and bias vector are obtained; finally, the multi-layer perceptron model predicts the features obtained after the adjacency matrix preprocessing of the same data set to obtain the final Classification results. The present invention obtains the eigenvalues ​​from the adjacency matrix corresponding to the network topology of the multi-agent system, which can simplify the matrix features, is more conducive to the learning of the perceptron, and the accuracy and precision of the learned artificial neural network are improved. it is good.

Description

Technical field [0001] The present invention belongs to the field of multi-intelligent system, and in particular, the present invention relates to a multi-intelligent system system based multi-layer perception system network accusation capability assessment method. Background technique [0002] With the development of robotics, computer, sensing, and communication technology, multi-agentsystems has caused a significant concern around the world, and has enormaborated social production and people's lives. Expertists at home and abroad have conducted deep research on the basic theories and key technologies of multi-intelligent system system in various aspects, and has achieved a large number of important results. Multi-intelligent system network topology features, system enabling capacity, safety level and survival capabilities, and corresponding preventive control measures are important in both theoretical and engineering. Therefore, it is necessary to analyze the network security ...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to provide a novel multi-layer perceptron-based multi-agent system network intrusion tolerance evaluation method for the problem that the traditional method cannot be effectively evaluated due to the large number of network nodes and high algorithm complexity, which can be applied in massive In the network composed of the number of nodes, the evaluation of the specific value of the (r, s) robustness attribute in the network topology graph, and then infer the intrusion tolerance of the network

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