An efficient method of P2P traffic identification based on wavelet packet decomposition and kernel principal component analysis

Peer‐to‐peer (P2P) traffic identification is currently an important challenge to network management and measurement. Many approaches based on statistics have been proposed to identify P2P traffic. However, flow features extracted by traditional methods are rough and one‐sided, which might lead to inaccuracy identification of network traffic. Besides, P2P traffic has too many statistical features, which is a challenge to the time complexity and space complexity of the classifier. This work focuses on the study of flow features. First, micro features of flow signals are extracted based on wavelet packet decomposition, and we combine them with the traditional features into combination features. The experimental results show that combination features have better performance than traditional features for P2P traffic identification, and 16 kinds of wavelet functions were tested to find the best one. Second, a feature reduction algorithm based on improved kernel principal component analysis is provided. The results show that the feature reduction algorithm proposed in this paper plays good performance to P2P traffic identification, because it could greatly reduced the number of features while having no affection on identification accuracy. Copyright © 2012 John Wiley & Sons, Ltd.