张利峰,邵斐

• 1:金陵科技学院计算机工程学院
• 2:金陵科技学院数据科学与智慧软件江苏省重点实验室

摘要(Abstract)：

采用支持向量机进行网络大数据平台异常风险监测时,建模效率低导致对异常风险的监测结果存在较高的误差,设计基于Hadoop的网络大数据平台异常风险监测系统。依据云计算Hadoop系统作业原理,通过Map/Reduce分布式模式对大数据进行分类筛选等操作,通过控制模块中的SDN控制器对大数据流量进行分流处理,将网络大数据分类反馈到监测模块中,采用监测模块通过预处理端和存储端对异常数据风险进行监测,通过预处理端实现大数据的有效分流监测;系统软件通过最小二乘支持向量机对网络大数据进行高效率建模,实现网络大数据异常监测。实验结果表明,所设计系统具有监测效率和稳定性高、性能佳的优势。

关键词(KeyWords)： 网络大数据;异常风险;监测系统;控制模块;Hadoop;最小二乘支持向量机

Abstract:

Keywords:

基金项目(Foundation): 江苏省教育厅批准项目(17KJB520008);; 江苏省高校自然科学研究重大项目(17KJA520001)~~

作者(Author): 张利峰,邵斐

DOI: 10.16652/j.issn.1004-373x.2018.22.035

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