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中文摘要：
通过研究网络异常检测，提出了一种基于流分解消除网络噪声的异常检测算法。该算法从高维、非平稳流量中分离出包含异常的随机部分，通过计算随机部分参数的边缘分布和残差，揭示了流量异常对随机部分参数的影响，并提出判断网络流量异常的参数标准。实验表明，由于不必将整个时间序列进行分片和单独拟合，算法可以直接处理非稳态流量数据，实现了真正意义上的网络异常检测功能。
英文摘要：
Diagnosing anomalies are difficult problem because one must extract and interpret anomalous patterns from large amounts of high-dimensional, noisy data. In this paper the network traffic is validated to possess a non-stationary characteristic and a general method was proposed to diagnose anomalies. This method is based on a separation of the non-stationary traffic into disjoint components corresponding to normal and anomalous network conditions. This separation can be performed effectively by both marginal distribution and qq-plot analysis of parameters of anomalous component. We evaluate the method＇ s ability to diagnose both existing and synthetically injected traffic anomalies in real traffic. Experiment shows the method can： ①accurately detect when traffic anomaly is occurring; ②does so with a very low false alarm rate.
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