Abstract: This paper proposes a real-time traffic classification model based on high-dimensional feature reduction clustering to address the problem of traditional network traffic classification methods being unable to avoid the impact of unknown traffic on classification and difficult to achieve real-time traffic classification. First， a CNN network model is built to extract high-dimensional features from traffic data， and save feature vectors. Then， UMAP is used to reduce the dimensionality of feature vectors， and the DBSCAN clustering algorithm is used to classify traffic， which effectively reduces the impact of unknown traffic on the model while achieving application-level classification. This paper proposes a time-delay control mechanism based on flow consistency， which borrows the idea of TCP congestion control mechanism and greatly reduces the time for traffic classification， making the model proposed in this paper able to meet the requirements of real-time traffic classification. At the same time， this paper collects a set of application-level traffic data sets in a real network. The experimental results on public data sets and the data set buiding by this paper show that the accuracy of this paper’s method is approximately 98% in the known data set， and when the unknown traffic is close to 50%， the accuracy remains at around 80%， and it can meet the requirements of real-time classification.

Key words: real-time traffic classification, feature dimensionality reduction, unknown traffic clustering, deep learning