Network data stream classification by deep packet inspection and machine learning

Abstract

How to accurately and efficiently complete the classification of Network data stream is an important research topic and a huge challenge in the field of Internet data analysis. Traditional port-based and DPI-based classification methods have obvious disadvantages in the increase category of P2P services and the problem of poor encryption resistance, leading to a sharp drop in classification coverage. Based on the original DPI classification, this paper proposes a method of network data stream classification using the combination of DPI and machine learning. This method uses DPI to detect network data streams of known features and uses machine learning methods to analyze unknown features and encrypted network data streams. Experiments show that this method can effectively improve the accuracy of network data stream classification.