Abnormal network information detection method based on knowledge graph

Abstract

The invention discloses an abnormal network information detection method based on a knowledge graph, and the method comprises the steps: polling an enterprise switch to capture related information in an industrial internet through employing an SNMP network protocol, thereby constructing a model, and achieving a data cleaning effect; secondly, abnormal and non-abnormal information is filtered through a probability statistics anomaly detection algorithm based on normal distribution, the screened non-abnormal information is filtered through a time dimension detection algorithm, and time points and other related data of IPv6 address number amplification anomaly under the time dimension are found out; and finally, establishing a dynamic knowledge graph to find out association information and association degrees among the abnormal information, and generating a downloadable text document which is displayed in a graph manner. According to the method provided by the invention, more valuable information can be analyzed from an industrial IPv6 network environment, and the accuracy and speed of abnormal condition detection are remarkably improved.

Description

technical field [0001] The invention relates to the technical field of network communication, in particular to a method for detecting abnormal network information based on a knowledge graph. Background technique [0002] With the continuous development of the main business of the enterprise, the dependence on the information system is getting higher and higher, and the problem of centralized management and control of information is becoming more and more prominent. The current society is advancing in the direction of "Internet of Everything", and the problems caused by the shortage of traditional IPv4 addresses are becoming more and more serious. Therefore, next-generation Internet technologies such as IPv6 technology have emerged. In the process of development, new requirements, new systems and new technologies are constantly being used, and network management is faced with the following problems, such as sharp increase in traffic and increased personnel workload, resulting...

AI Technical Summary

Problems solved by technology

In the process of development, with the continuous use of new requirements, new systems and new technologies, network management is faced with the following problems, such as a sharp increase in traffic and increased workload of personnel, resulting in increased burden; secondly, traditional monitoring methods are too scattered, which is not conducive to faults The accurate positioning of the data, the complex relationship between the data is not conducive to quickly find out the key to the problem, and passively accept various problems and failures, lack of independent coping strategies

[0003] For the proposed anomaly detection scheme, there are still many defects. In most practical scenarios, the data itself has no labels, and some data sets have labels, but the reliability of the labels is very low, which leads to putting them into the model. The after effect is very poor, which prevents us from directly using some mature supervised learning methods

In some fraud detection scenarios, various fraud data are mixed together, it is difficult to distinguish different types of fraud, insufficient consideration is given to the exploration of the time dimension, and there is a lack of response to abnormal analysis within the time interval

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