A Micro-Cluster-Based Data Stream Clustering Method for P2P Traffic Classification

Abstract:

Many machine learning techniques were proposed to classify P2P traffic and each with reasonable successes. But in the real P2P network environment, new communities of peers often attend and old communities of peers often leave. It requires the identification methods to be capable of coping with concept drift and updating the model incrementally. In this paper, we presented a concept-adapting algorithm MCStream which was based on streaming data mining techniques to identify P2P applications in Internet traffic. The MCStream used two micro-cluster structures, potential micro-cluster structures and outlier micro-cluster structures, to classify the P2P traffic and discovered the concept drift with limited memory. Our performance studied over a number of real data which was captured at a main gateway router demonstrates the effectiveness and efficiency of our method.

• Periodical: Applied Mechanics and Materials (Volumes 263-266)
• Pages: 1121-1126
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.263-266.1121
• Online since: December 2012
• Authors: Guang Hui Yan, Ming Hao Ai
• Keywords: Concept Drift, Micro-Cluster Based Clustering, Peer-to-Peer Traffic Identification

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