Intelligent unmanned equipment group network anomaly detection method and system based on machine learning

Abstract

The invention discloses an intelligent unmanned equipment group network anomaly detection method and system based on machine learning, and the method comprises the steps: obtaining the driving data of each piece of intelligent unmanned equipment in an intelligent unmanned equipment group network and the network data generated by mutual communication in the group network, extracting data with different features to represent a driving state and a network state of the intelligent unmanned equipment group network in a driving process, and converting the driving state and the network state into feature vectors; performing dimensionality reduction on the feature vectors, performing driving event clustering of the intelligent unmanned equipment on the feature vectors subjected to dimensionality reduction according to density distribution, and dividing the driving events of the intelligent unmanned equipment in the intelligent unmanned equipment group network into normal events and abnormal events; and for the divided driving events, integrating the corresponding feature vectors into a corresponding matrix, inputting the matrix into a machine learning model, learning the features of normal events and abnormal events, and carrying out anomaly detection on intelligent unmanned equipment group network by using the model. According to the method, the driving data and the network data are combined, and efficient anomaly detection can be realized.

Description

technical field [0001] The invention belongs to the field of abnormal detection of networking, and in particular relates to a method and system for detecting abnormality of intelligent unmanned equipment networking based on machine learning. Background technique [0002] Intelligent unmanned equipment networking is a group of equipment composed of multiple reusable intelligent unmanned equipment operated by artificial radio remote control or autonomous program control devices, such as unmanned vehicles, unmanned aerial vehicles, unmanned boats, etc. . Due to their strong maneuverability, high degree of completion, and flexible ability to perform tasks, they have been widely used in many areas of life, such as agricultural irrigation, logistics transportation, emergency rescue and disaster relief, aerial photography, military monitoring, etc. Compared with the shortcomings of a single intelligent unmanned device performing tasks due to limited load and small task scope, whic...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to provide a machine learning-based intelligent The abnormality detection method and syst...

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