Network data intrusion detection method, system, terminal and storage medium

Abstract

The present invention provides a network data intrusion detection method, system, terminal and storage medium, comprising: collecting network transmission data as sample data and dividing the sample data into multiple groups, the network transmission data including normal data and intrusion data; Use the FCBF algorithm to remove redundant features that are not related to the preset target category in each group, and use the data group that removes redundant features as a kinship set; perform single-point genetic crossover processing on the kinship set, from the kinship set and the genetic Screen out the initial set with the highest correlation with the target category from the subset set; use the genetic variation algorithm to calculate the variation set of the initial set, and compare the correlation between the initial set and the target category and the correlation between the variation set and the target category , iteratively filtering out the data set with the highest correlation with the target category; using the data set with the highest correlation with the target category as a feature subset, and performing feature extraction and feature detection on the network transmission data according to the feature subset.

Description

technical field [0001] The invention relates to the technical field of network intrusion detection, in particular to a network data intrusion detection method, system, terminal and storage medium. Background technique [0002] With the rapid development of big data and cloud computing, network intrusion methods have the characteristics of concealment and silence, and people's demand for network security is increasing day by day. However, with the increase of the amount of data, the mainstream network intrusion models are becoming more and more inefficient in intrusion identification. Because these data are not only large in scale, but also high in dimension, there is a lot of redundant information, and these irrelevant information will greatly reduce the effect of intrusion detection. Feature selection is an effective method to solve this problem. A good feature selection algorithm can effectively eliminate redundant features or noise data in classified data, and improve th...

AI Technical Summary

Problems solved by technology

However, the feature subset obtained in this way is prone to overfitting. Once the sample set changes, the feature subset will not be applicable to the new sample set. Using the original feature subset for classification will lead to a decrease in classification accuracy.

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