大数据背景下网络流量数据检测的数学模型分析

Gui-hua Li, Yun-xu Tong
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引用次数: 0

摘要

传统的网络流量数据检测存在工作效率低、检测范围小的问题。为此,设计了大数据背景下网络流量数据检测的数学模型。确定了网络流量数据检测的原理,提取了网络流量数据的多尺度特征,建立了基于理论的网络流量数据监控结构。在该结构中对网络数据输入和输出节点进行了计算,实现了网络流量数据检测数学模型的构建。实验结果表明,与传统方法相比,该网络流量数据检测数学模型提高了8%,检测距离提高了2倍,且检测精度高,具有实用性(l4摘要)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Mathematical Model Analysis of Network Traffic Data Detection Under the Background of Big Data
The traditional network traffic data detection had the problems of low work efficiency and small detection range. To this end, the mathematical model of network traffic data detection under the background of big data was designed. The principle of network traffic data detection is determined, multi-scale features of network traffic data are extracted, and a network traffic data monitoring structure based on theory is established. The network data input and output nodes were calculated in the structure to realize the construction of the mathematical model of network traffic data detection. The experimental results showed that compared with the traditional method, the mathematical model of network traffic data detection had an improvement of 8% and a detection range of 2 times, and the detection accuracy is high, has the practicality(L4bstract).
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