准确识别对等网络(P2P)流量对网络流量控制有着重要意义。针对P2P流量提出一种高准确度的识别方法。该方法通过统计报文首部ASCII码出现的频率，提取出一个256维的统计特征，结合数据流量的传输层特征，使用决策树算法对流量进行分类识别。在识别过程中提出数据分块的思想，提高了识别的正确率并且能够统计P2P流量流经的端口。仿真测试结果表明，该方法可以在多种流量混杂的情况下识别出P2P流量，且具有较高的准确度。

Identifying Peer-to-Peer(P2P) traffic accurately has important influence on network flow control. A new P2P traffic identification method with high accuracy is proposed. This method calculates the frequency of 256 ASCII bytes occuring in packet header and turns it into a 256 dimensional statistical feature. Combining transport layer features and packet header statistical feature, this method identifies P2P traffic by means of decision tree algorithm. Data deblocking is proposed to maintain high accuracy and collect port numbers that relate to P2P traffic. The experimental results demonstrate that this method can distinguish P2P traffic from non-P2P traffic in different situations with high accuracy.