Traffic identification method based on bag of word (BOW) model and statistic features

Abstract

The invention discloses a traffic identification method based on a bag of word (BOW) model and statistic features. The method adopts the BOW model matched with a feature extraction method, trains collected network traffic features, and thus obtains a feature vector corresponding to each network category. For new network traffic, similarly, by extracting traffic features, utilizing the BOW module to obtain a corresponding feature vector, and then sequentially comparing with a feature vector of each network category which is previously established, the category corresponding to the feature vector with highest matching degree serves as a category tag of the new network traffic. The BOW method combines with an unsupervised k-means clustering method and a supervised k-nearest neighbor method, thereby being more suitable for multi-category classification. Due to the fact that the BOW model is not sensitive to space position, during feature extraction, arrangement according to time series of features is not required, and processing is convenient.

Description

technical field [0001] The invention relates to a method for identifying network data streams, in particular adopting a machine learning model of BoW (Bag of Words) in conjunction with a proposed feature extraction method for predictive modeling. Background technique [0002] At the end of the 1990s and the beginning of this century, batches of experiments and attempts on Internet traffic classification technology emerged, including revolutionary technological innovations. One of the main driving forces of scientific and technological research is the actual application requirements. Throughout the entire Internet development process, traffic identification mainly plays an extremely important role in the following aspects: [0003] ●Internet service providers (ISPs) need to know which applications their users are using, or the development trend of obtaining applications, so as to implement various business goals. Such as dynamically allocating network resources for users of ...

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