Novel Non-stationary Time Series Anomaly Detection Model Based on Superstatistics Theory
一种基于超统计理论的非平稳时间序列异常点检测方法研究

Because of network traffic non-stationary property it can hardly use traditional way to analyze the complicated network traffic. A new detection method of non-stationary network traffic based on superstatistics theory was discussed. According to the superstatistics theory, the complex dynamic system may have a large fluctuation of intensive quantities on large time scales which causes the system to behave as non-stationary which is also the characteristic of network traffic. This new idea provides us with a novel method to partition the non-stationary traffic time series into small stationary segments. We used the slow parameters of the segments as a key determinant factor of the system to describe the network characteristic and analyze the slow parameters with time series theory to detect network anomaly.The result of experiments indicates that this method can be effective.