基于云计算的海量网络流量数据分析处理及关键算法研究

Analysis and Processing of Massive Network Traffic Data Based onCloud Computing and Research on Key Algorithms

为了应对日益增长的网络流量数据量和对网络安全的需求,提高网络流量数据的处理效率和准确性,文中从云计算架构出发,设计并搭建了一个能承载大规模网络流量数据处理的云计算平台。基于该平台,采用了分布式存储、并行计算和机器学习等技术,对海量网络流量数据的预处理、聚类分析、异常检测等关键环节进行了研究。结果表明,基于云计算的海量网络流量数据分析处理的关键算法取得了显著成果。通过分布式存储和并行计算技术,实现了对海量网络流量数据的高效读写和处理。在预处理阶段,针对流量数据进行采样和滤波,减少了数据量,并保留了关键特征。在聚类分析方面,利用机器学习算法实现了对网络流量的分类和统计,通过构建模型、训练和优化算法,实现了对网络攻击和异常行为的准确识别和及时报警。

In order to cope with the increasing amount of network traffice data and the demand for network security,improve the processing eficiency and accuracy of network traffic data,based on the cloud computing architecture,this paper designs and builds a cloud computing platform capable of carrying large scale network traffie data processing.At the same time,based on the plat-form,distributed storage,parallel computing and machine learning technologies are adopted to cary out research on the key links of mass network traffic data pre processing,cluster analysis and anomaly detection.The results show that the key algorithms for analyzing and processing massive network traffice data based on cloud computing have achieved remarkable results.Through the distributed storage and parallel computing technology,it realizes the efficient reading and writing and processing of massive net-work traffic data.In the pre processing phase,the flow data is sampled and filtered,which significantly reduces the data volume and retains key features.In terms of cluster analysis,the cassification and statistics of network traffice are realized by using machine learning algorithms,and the accurate identification and timely alarm of network attacks and abnormal behaviors are realized by building models,training and optimizing algorithms.