Network Malicious Traffic Detection Algorithm Based on CNN+GRU
In order to solve the problems that the low accuracy and efficiency of network malicious traffic detec-tion,this paper proposes a network abnormal traffic detection model based on CNN+GRU algorithm(CN-RU).The model uses convolutional neural network and gated recurrent unit to automatically extract the spatial and temporal characteristics of traffic respectively and collect the network traffic characteristics in an all-around way.The model u-ses multiple small convolution kernels and gated recurrent units with few parameters to accurately extract traffic fea-tures and reduce model parameters to achieve the purpose of improving detection accuracy and efficiency.In the ex-periment,ISCX IDS2012,CIC-IDS2017 and UNSW-NB15 were used to evaluate the effect,and network traffic detec-tion models with different algorithms were compared.The experimental results show that the CNN+GRU structure model proposed in this paper solves the problem of gradient disappearance in the neural network model and greatly improves the accuracy and detection efficiency.The model has higher application value and better universality in net-work security management.
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