一种基于本体的并行网络流量分类方法

发布时间：2018-06-23 20:14

  本文选题：知识推理 + MapReduce　； 参考：《电子科技大学学报》2016年03期

【摘要】：海量网络流量数据的处理与单一节点的计算能力瓶颈这一矛盾导致数据分类效率低,无法满足现实需求。为解决这一问题,结合本体与MapReduce技术各自在海量异构数据描述与处理方面的优势,提出一种基于本体的并行网络流量分类方法。该方法基于MapReduce并行计算架构,根据网络流量本体结构,对网络流量本体并行化构建;通过并行知识推理完成基于流量统计特征的网络流量分类。实验结果表明,集群环境下基于MapReduce的网络流量本体构建效率明显高于单机环境,而且适当增加计算节点使得加速比线性提升;并行知识推理的分类方法能够有效地提高大规模网络流量的分类效率。
[Abstract]:The contradiction between the processing of massive network traffic data and the bottleneck of computing power of a single node leads to the low efficiency of data classification and can not meet the actual needs. To solve this problem, combining the advantages of ontology and MapReduce in describing and processing massive heterogeneous data, a parallel network traffic classification method based on ontology is proposed. The method is based on the MapReduce parallel computing architecture, constructs the network traffic ontology parallelization according to the network traffic ontology structure, and accomplishes the network traffic classification based on the traffic statistics by parallel knowledge reasoning. The experimental results show that the efficiency of constructing network traffic ontology based on MapReduce in cluster environment is obviously higher than that in single computer environment, and the increase of computing nodes makes the speedup linear. The classification method of parallel knowledge reasoning can effectively improve the classification efficiency of large scale network traffic.
【作者单位】： 桂林电子科技大学认知无线电与信息处理省部共建教育部重点实验室;桂林电子科技大学广西高校云计算与复杂系统重点实验室;桂林电子科技大学广西可信软件重点实验室;
【基金】：国家自然科学基金(61163058,61363006) 广西可信软件重点实验室开放课题(KX201306) 广西高校云计算与复杂系统重点实验室开放课题(14104)
【分类号】：TP393.06
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本文编号：2058264