基于大数据挖掘的网络流量异常检测算法

Network Traffic Abnormal Detection Algorithm Based on Big Data Mining

当前检测网络中存在的异常流量是防止异常流量攻击网络的有效策略之一。本文首先建立了网络流量稳态模型,挖掘并剔除了了网络流量中存在的坏数据。然后通过S变换及其逆变换重构网络流量数据,提高了检测精度。最后,提取网络流量特征,在此基础上完成了网络流量异常检测。实验结果表明,所提方法可适用于不同类型网络流量的异常检测,具有良好的检测性能。

Currently, detecting abnormal traffic existing in the network is one of the effective strategies to prevent abnormal traffic from attacking the network. In this paper, the steady state model of network traffic is established firstly, and the bad data existing in the network traffic is mined and eliminated. Then the network traffic data is reconstructed through S transform and its inverse transform, which improves the detection accuracy. Finally, the network traffic characteristics are extracted, and the network traffic abnormal detection is completed on this basis. The experimental results show that the proposed method can be applied to abnormal detection of different types of network traffic, and has good detection performance.