自动检测网络流量异常和变化

Astha Syal, A. Lazar, Jinoh Kim, A. Sim, Kesheng Wu
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引用次数: 8

摘要

准确预测网络行为有利于TCP拥塞控制,有助于改进路由、分配网络资源和优化网络设计。这项任务具有挑战性,因为许多因素可能会影响网络流量,例如网络会话的数量和综合重新排序。还有许多方法可以测量网络状态,例如每个流的重传次数和数据包重复。在这项工作中,我们使用了一组在多个数据传输节点(DTN)上的主要计算机中心收集的被动TCP流量测量数据。为了帮助计算机网络的运行,我们建议实时检测异常缓慢的网络传输。该系统将网络监控日志分解为固定大小的块,并使用最先进的分类器来识别慢时间窗口。该方法将在多个ddn的真实大数据集上进行验证。该方法能够生成快速检测大间隔低性能网络传输的模型,这需要网络工程师的关注。
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Automatic Detection of Network Traffic Anomalies and Changes
Accurately predicting network behavior is beneficial for TCP congestion control, and can help improve routing, allocating network resources, and optimizing network designs.This task is challenging because many factors could affect network traffic, such as the number of network sessions and synthetic reordering. There are also many ways to measure the network state, such as the number of retransmissions per flow and packet duplication. For this work, we use a set of passive TCP flow measurements collected at a major computer center on multiple data transfer nodes (DTN). To assist the operations of the computer network, we propose to detect abnormally slow network transfers in real-time. The proposed system breaks the network monitoring logs into fixed-size chunks and employs a state of art classifier to identify the slow time windows. This method will be validated on real large datasets collected from several DTNs. The proposed method is able to generate models to quickly detect large intervals of low performing network transfers, which require attention from network engineers.
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