Classification and Detection Method of Communication Network Attack Behavior Based on Support Vector Machine
Due to the high dimensionality and complexity of communication network traffic data,traditional methods for detecting network attack behavior have lower accuracy.In order to improve the detection accuracy,this paper proposes a classification and detection method of communication network attack behavior based on support vector machines.This method uses preprocessed traffic data to construct a graph convolutional neural network model,extracts features,and inputs them into support vector machines for classification,obtaining the final classification result of attack behavior.The simulation experiments results show that the false positive rate of classification detection results based on graph convolutional neural networks is only 0.78％,which has higher detection accuracy compared to the classification detection methods based on BP neural networks and ordinary convolutional neural networks.
support vector machinecommunication networkattack behaviorclassification detection
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