Periodicity-Based Anomalies in Self-Similar Network Traffic Flow Measurements
Periodicity-Based Anomalies in Self-Similar Network Traffic Flow Measurements
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DOI：
10.1109/tim.2010.2084711
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发表时间：
2011-04
影响因子：
5.6
通讯作者：
Tayfun Akgül;S. Baykut;M. Erol-Kantarci;S. Oktug
中科院分区：
文献类型：
--
作者：
Tayfun Akgül;S. Baykut;M. Erol-Kantarci;S. Oktug
网络流量测量是及时监测计算机网络和诊断潜在异常的基础。以往的测量研究表明，网络流量往往是自相似的。自相似性的程度由Hurst参数H描述。在文献中，各种方法已被用于估计H，而它们的性能还没有被评估的网络痕迹，包含基于纯度的异常。在本文中，我们调查的性能知名的估计交通流量测量与基于密度的异常。我们推导出广泛使用的估计方法在时间，频率，小波和本征域的解析表达式，并通过模拟表明，基于结晶度的异常影响赫斯特参数估计，造成不可靠的H估计。我们表明，我们的理论和实验结果是一致的观察真实的网络流量测量。
Network traffic flow measurement is fundamental in timely monitoring computer networks and in diagnosing potential anomalies. Previous measurement studies have shown that network traffic flows are often self-similar. The degree of self-similarity is described by the Hurst parameter H. In the literature, various methods have been used in estimating H, while their performances have not been evaluated for network traces that contain periodicity-based anomalies. In this paper, we investigate the performance of well-known estimators for traffic flow measurements with periodicity-based anomalies. We derive analytical expressions for widely used estimation methods in time, frequency, wavelet, and eigen domains and demonstrate through simulations that periodicity-based anomalies affect Hurst parameter estimation, causing unreliable H estimates. We show that our theoretical and experimental results are consistent with the observations of real network traffic flow measurements.