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

An anomaly detection and machine learning technology, which is applied in the field of abnormal detection of intelligent unmanned equipment networking, can solve problems such as poor detection effect of intelligent unmanned equipment networking anomalies, difficulty in abnormal detection of driving data and network data, etc.

Active Publication Date: 2021-09-10
XIDIAN UNIV
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  • Application Information

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Problems solved by technology

[0004] The purpose of the present invention is to provide a machine learning-based intelligent The abnormality detection method and system of the unmanned equipment network can quickly and efficiently realize the abnormality detection of the intelligent unmanned equipment network

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

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Embodiment Construction

[0035] The present invention will be described in further detail below in conjunction with the accompanying drawings.

[0036] A machine-learning-based intelligent unmanned equipment networking anomaly detection method proposed by the present invention mainly consists of two stages: the machine learning stage and the anomaly detection stage, see figure 1 process shown.

[0037]First, the driving data and network data in the intelligent unmanned equipment network are extracted, and the PCA algorithm is used to reduce the dimensionality of all the extracted feature vectors, and then the DBSCAN algorithm is used to perform density clustering on the reduced features, which are divided into normal events and exceptions. Then input the feature vector of the divided event into the CNN algorithm for feature extraction, and then input it into the SVM algorithm to realize the binary classification, so that the abnormal detection of the intelligent unmanned device network can be realize...

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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...

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Application Information

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IPC IPC(8): H04W12/121H04W12/00H04L29/06G06N3/04G06N3/08G06N20/10
CPCH04W12/121H04W12/009H04L63/1425G06N3/08G06N20/10G06N3/047G06N3/045
Inventor 李腾方保坤乔伟廖艾林杨旭孙小敏马卓马建峰
Owner XIDIAN UNIV
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