关键词: P2P流量识别, 云计算, 粗糙集, 朴素贝叶斯

Abstract: Due to memory limitations, P2P traffic identification can only deal with small-scale data sets in a stand-alone environment. And all the attribute characteristics used in the P2P traffic identification based on Bayesian classification are artificial selected. Therefore, the recognition rate is both restricted and lack of objectivity. Based on the above analysis, a Naive Bayesian classification algorithm is proposed in the cloud computing environment, and then an attribute reduction algorithm is improved to adapt to the cloud computing environment. Finally, both above algorithms are combined to achieve fine-grained encrypted P2P traffic identification. The experimental results show that this method can efficiently process large data sets of network traffic, and the recognition rate of P2P flow is high, and the results are objective at the same time.

Key words: P2P traffic identication, cloud computing, rough set, Naive Bayesian