基于大数据分析的网络异常节点智能定位系统

Network abnormal node intelligent positioning system based on big data analysis

传统网络异常节点定位系统对于复杂多样且高速流动的数据处理能力较低,导致系统定位检测误报率较高,因此文中设计一种基于大数据分析的网络异常节点智能定位系统。系统设计中的硬件部分沿用了原有系统中的硬件,主要对软件部分的大数据分析以及节点定位算法进行深入研究。系统数据采集主要应用Flume数据采集组件来适配复杂数据环境,构建Spark集群数据处理框架,通过采集到的数据计算出节点的风险值,在定位算法中,部署网络模型,通过计算路由器节点的相邻节点数量判断节点的情况,完成网络异常节点的智能定位。在仿真实验中,对设计的系统与原有系统中恶意丢包假阳性、假阴性概率与恶意改包假阳性、假阴性概率进行对比得到结论,设计的系统能够有效降低误报率。

The traditional network abnormal node location system has low processing ability for complex and high⁃speed flow data,which leads to high false alarm rate of positioning detection of the system.Therefore,a network abnormal node intelligent positioning system based on big data analysis is designed.In the system design,the hardware part still adopts the hardware in the original system,and the studies in software part focus mainly on the big data analysis and node positioning algorithm.The Flume data acquisition component is used in data acquisition of the system to adapt to the complex data environment.The Spark cluster data processing framework is constructed to calculate the risk value of the node according to the collected data.The network model is deployed in the localization algorithm.The situation of the node is judged by calculating the number of nodes adjacent to the router node.The intelligent positioning of network abnormal nodes is completed.In the simulation experiment,the false positive and false negative probability of malicious packet loss was compared with the false positive and false negative probability of malicious packet change of the designed system and origin system.The conclusion shows that the designed system can effectively reduce the false positive rate.