Rapid Recognition of Abnormal Traffic in the Communication Network Based on Deep Learning
There are many kinds of abnormal traffic in the communication network,including various types of attacks,malicious behaviors,etc.,and has very complex and high-level abstract characteristics,which cannot capture the local abnormal behavior,making the efficiency of iden-tifying abnormal traffic low.Therefore,the convolutional neural network is introduced to pro-pose a fast identification method of abnormal traffic in the communication network based on deep learning.Using Wireshark tool to obtain and fuse network traffic data,using the weighted average filtering method,select the convolution neural network Convolutional Neural Net-work,CNN)as the abnormal flow feature extraction algorithm,input the local features in the convolution layer,realize the abnormal flow feature extraction.The overall similarity of the ab-normal traffic characteristics is calculated,the abnormal traffic threshold is set,and the abnormal traffic is recognized quickly through the abnormal probability of the traffic in the communica-tion network.The experimental results show that the recall rate of the proposed algorithm is up to 0.96,and in the case of 200 byte data,the identification delay is only 9.2ms,which provides effective support for the application scenario of quickly identifying and processing abnormal flow.
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