Network traffic abnormality detection method based on SVM (Support Vector Machine)

Abstract

The invention discloses a network traffic abnormality detection method based on an SVM (Support Vector Machine), which comprises the steps of reading historical network traffic data; extracting network traffic features of the historical network traffic data; carrying out data standardization on the network traffic features; carrying out reduction on the network traffic features to obtain simplified and optimized feature subsets; and training the optimal feature subset by utilizing the SVM to obtain an SVM classifier; adding processed online test network traffic data into the SVM classifier, carrying out calculation by the SVM classifier to obtain a final classification result, and determining whether the processed online test network traffic data is abnormal network traffic data. Compared with the prior art, according to the network traffic abnormality detection method disclosed by the invention, network traffic feature data is subjected to feature reduction and dimensionality reduction by a PCA-TS (Principal Component Analysis-Tabu Search) method, and the optimal feature subset is selected. The problems of long classification detection time, low efficiency and occupation for a larger storage space, which are brought by the curse of dimensionality, are avoided; and moreover, processing time is reduced for subsequent processing, and classification accuracy of the classifier is improved.

Description

technical field [0001] The invention relates to the technical field of network security, in particular to an SVM-based network traffic anomaly detection method. Background technique [0002] With the popularization of mobile communication networks and the opening of the era, my country's mobile Internet has entered a stage of vigorous development. However, with the maturity of Internet technology and the continuous expansion of the market, a large amount of network traffic data has been generated. This data has high value and high dimensionality. Large numbers and other characteristics, in the process of use and storage, it is very easy to become the target of hackers. In recent years, various attacks on the Internet have occurred extremely frequently, seriously threatening the normal use of the network, and the importance of Internet security has become increasingly prominent. Therefore, how to timely and effectively detect network anomalies and ensure a safe network enviro...

AI Technical Summary

Problems solved by technology

[0002] With the popularization of mobile communication networks and the opening of the era, my country's mobile Internet has entered a stage of vigorous development. However, with the maturity of Internet technology and the continuous expansion of the market, a large amount of network traffic data has been generated. This data has high value and high dimensionality. Large numbers and other characteristics, in the process of use and storage, it is very easy to become the target of hackers

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