基于大数据的网络信息数据流量异常检测方法

Big Data Based Network Information Data Flow Anomaly Detection Method

网络结构日益复杂,网络故障出现的频率也逐渐增多,对网络管理带来了挑战。在网络管理中,最重要的任务是通过对网络信息数据流量异常情况进行检测,提前进行处置,将故障率降低到最低。对此,笔者提出基于大数据的网络信息数据流量异常检测方法。通过对比实验证明,该方法具有更高的准确率,且检测性能良好,能够满足目前大数据环境下对网络信息数据流量异常的检测要求。

The network structure is increasingly complex,and the frequency of network failures is gradually increasing,posing a challenge to network management.In network management,the most important task is to detect the abnor mality of network information data traffic and deal with it in advance to minimize the failure rate.In this regard,a method for detecting abnormality of network information data traffic based on big data is proposed.The comparison experiments show that the method has higher accuracy and good detection performance,which can meet the requirements of detecting abnormal network information data flow in the current big data environment.