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Intelligent detection method for abnormal big data in heterogeneous networks based on Bayesian classification

机译：基于贝叶斯分类的异构网络异常大数据智能检测方法

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摘要

In order to overcome the existing abnormal big data intelligent detection method, the problem of low detection accuracy and poor convergence is not carried out without abnormal big data classification. A new Bayesian classification based heterogeneous network anomaly big data intelligent detection is proposed in this paper. method. Design an abnormal big data intelligent detection architecture, use TcpDump collection tool to collect and process heterogeneous network traffic data, and build the relationship between bottleneck traffic and abnormal big data based on the processed data, through Fourier transform The method obtains the data frequency information and uses the Bayesian network classification method to realize the intelligent detection of abnormal big data in heterogeneous networks. The experimental results show that compared with the traditional method, the proposed method greatly improves the detection accuracy, convergence and anti-interference, and fully demonstrates that the proposed method has better detection effect.
机译：为了克服现有的异常大数据智能检测方法，未在没有异常大数据分类的情况下进行低检测精度和收敛差的问题。本文提出了一种新的贝叶斯分类基于异构网络异常大数据智能检测。方法。设计异常的大数据智能检测架构，使用TCPDump收集工具收集和处理异构网络流量数据，并基于处理数据的瓶颈交通和异常大数据之间的关系，通过傅立叶变换来获得数据频率信息和使用贝叶斯网络分类方法来实现异构网络中异常大数据的智能检测。实验结果表明，与传统方法相比，所提出的方法大大提高了检测精度，收敛性和抗干扰，并完全证明了所提出的方法具有更好的检测效果。

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