IP traffic classification based on machine learning

With the rapid development of Internet, many network applications (e.g., P2P) use dynamic ports and encryption technology, which makes the traditional port and payload-based classification methods ineffective. Hence, it is important and necessary to find the more effective ones. Currently the machine learning (ML) techniques provide a promising alternative one for IP traffic classification. In this work, we use the ML-based classification method to identify the classes of the unknown flows using the payload-independent statistical features such as packet-length and arrival-interval. In order to improve the efficiency of the classification methods, the feature reduction techniques are further adopted to refine the selected features for attaining a best group of features. Finally we compare and evaluate the ML classification algorithms based on the BRASIL data source in terms of the three metrics such as overall accuracy, average precision and average recall. Our experiments show that the decision-tree algorithm is the best ML one for IP traffic classification and is able to construct the real-time classification system.