一种基于无监督学习的社交网络流量快速识别方法

在分析了社交网络的发展和研究现状后,结合现有的网络流量识别方法和社交网络流量特征属性,提出了一种基于KMeans聚类算法的无监督学习社交网络流量识别方法.为了提高处理的高效性、实时性,利用开源云计算平台如doop上提供的M印Reduce架构进行分布式并行处理.对比实验结果表明,提出的方法能快速、高效的识别社交网络流量,并且识别准确率有显著提高.

A Fast Identification Approach to Social Network Traffic Based on Unsupervised Learning

We combine existing methods of network traffic identification and the features of social network flow after analyzing the development and research statement of social network.We proposed an unsupervised social network flow identification method based on KMeans clustering algorithm.In order to improve the efficiency of processing,real-time,we use the MapReduce model which is provided by the open source software Hadoop,which makes it distributed and parallel.Through experiments and comparison with existing work,it is proved that our method can recognize the social network flow efficiently,and the accuracy is significantly improved.