Research on Flow Classification Model Based on Similarity and Machine Learning Algorithm

Abstract

In recent years, with the rapid development of the Internet, complex and diverse applications and network traffic have been generated. At the same time, network encryption technologies and various new network traffic have emerged, which affects the efficiency of the original traffic classification technology. In order to improve the efficiency of traffic classification and reduce the classification time, this paper proposes a network traffic classification model (Cosine similarity and decision tree classification model, CSDT) based on cosine similarity and decision tree algorithm to identify and classify traffic. First, the cosine similarity algorithm is used to judge the similarity of adjacent network traffic, and the network traffic with higher similarity is labeled with a known classification and forwarded. For network traffic with low similarity, the decision tree algorithm is used to classify the related feature values. This model utilizes the characteristics of high similarity in adjacent data streams, and uses similarity algorithms to preprocess network traffic to reduce classification time. The Moore data set publicly available in the field of network traffic classification is used for training and testing, and the results are compared with various machine learning algorithms on the Weka platform. The experimental results show that the model has a good classification accuracy, which greatly reduces the classification time and improves the classification efficiency of network traffic is improved.