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A Clustering Analysis Method for Network Traffic Based on Feature Parameter Distribution

机译:基于特征参数分布的网络流量聚类分析方法

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Network traffic analysis needs a lot of data which include much information. Predominating pattern state of traffic true and roundly has been an active and difficult research topic in the field of traffic analysis for many years. Up to now, simplex data analyzed, requirement of high dependence to analyst and the distortion of analyzed result because of some noisy data in the complicated network still make it not perform as well as expected in practice. In view of the situation, this paper proposes the distribution of traffic feature parameters as researching object and making a clustering with an improved algorithm to realize changing tendence and state of traffic. This method is used by making a test with much real data captured by SNMP agent, and the result of experiment indicates that it can eliminate disturbance in the fact of person basically and make a non- supervised analysis furthest. This analysis method is sensitive to changes of traffic pattern and the analysis result is effective with a general real-time requirement.
机译:网络流量分析需要大量包含大量信息的数据。多年来,在交通分析领域,以真实,完整的交通模式状态为主导一直是一个活跃而又艰巨的研究课题。到目前为止,由于复杂网络中存在一些嘈杂的数据,分析的单纯数据,对分析人员的高度依赖以及分析结果的失真仍使其表现不如预期。针对这种情况,提出以交通特征参数的分布为研究对象,并用改进的算法进行聚类,以实现交通的变化趋势和状态。通过对SNMP代理捕获的大量真实数据进行测试来使用该方法,并且实验结果表明,该方法可以基本上消除人为因素的干扰,并且可以进行最大程度的无监督分析。这种分析方法对交通模式的变化敏感,并且分析结果对一般的实时要求是有效的。

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