A fault detection method and device for network management system

Abstract

The invention discloses a fault detection method oriented to a network management system, comprising the following steps: Step 1, obtaining the original unbalanced network fault data set NF, compressing and filtering the NF, and obtaining the processed data sets NF', NF' Including samples, the samples include two types of samples, and the two types of samples refer to non-fault samples and fault samples; Step 2, obtaining the unbalanced ratio I, the unbalanced ratio I=the number of non-fault samples / the number of fault samples; Step 3, pre-set a ratio of the number of samples of the two types after sampling, according to the number N of newly synthesized fault data samples obtained; Step 4, construct the spatial distribution allocation rule of the newly synthesized fault data samples; Synthetic rules of data samples; step 6, establishing an XGB‑RF network fault detection model. The invention effectively improves the accuracy rate of network fault detection.

Description

technical field [0001] The invention relates to the technical fields of electronics, communication and information engineering, in particular to a fault detection method and device for a network management system. Background technique [0002] Faced with the current increasingly complex and large-scale network systems, the difficulty of network maintenance has also increased exponentially. Once the network fails, it will cause disastrous consequences no matter in terms of military defense, business, or residents' daily life. Estimated loss and inconvenience. Therefore, how to effectively use the current technology, discover faults, diagnose faults, and restore faults is of great significance to ensure the smooth operation of the network management system and maintain the normal and orderly development of the social environment. [0003] Some traditional data analysis methods are difficult to obtain satisfactory results in the face of increasingly large and complex network b...

AI Technical Summary

Problems solved by technology

However, since SMOTE treats all minority samples equally and does not consider the category information of neighboring samples, sample aliasing often occurs, resulting in poor classification results.

In addition, if the selected minority class samples are surrounded by minority class samples, the newly synthesized samples will not provide much useful information; if the selected minority class samples are surrounded by majority class samples, such samples may be noise, then The newly synthesized samples will have a large overlap with the surrounding majority class samples, making classification difficult

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