基于时间序列分析的网络流量异常检测

Network Traffic Anomaly Detection Based on Time Series Analysis

针对传统模型无法对网络流量异常进行准确识别和检测的问题,提出一种基于时间序列分析的网络流量异常检测模型.首先提取网络流量的原始数据,并对原始数据进行小波阈值去噪处理,消除干扰因素的影响;然后采用时间序列分析法挖掘网络流量数据之间的变化关系,建立网络流量异常检测模型;最后通过仿真实验验证检测模型的有效性和优越性.实验结果表明,时间序列分析法可以准确、及时地检测网络流量的异常行为,且结果优于目前其他网络流量异常检测模型.

Aiming at the problem that the traditional model could not accurately identify and detect network traffic anomalies,we proposed a network traffic anomaly detection model based on time series analysis.Firstly,the original data of network traffic was extracted,and the original data was denoised by wavelet threshold to eliminate the influence of interference factors.Secondly,time series analysis method was used to mine the relationship among network traffic data,and network traffic anomaly detection model was established.Finally,simulation experiments were used to verify the effectiveness and superiority of the detection model.The result shows that time series analysis can accurately and timely detect abnormal behavior of network traffic,and the detection results are better than other current network traffic anomaly detection models.