基于PCA和GMM的宽带网络流量异常检测方法

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原文链接

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中文摘要：随着网络规模和复杂度的不断提升,宽带网络流量异常检测成为保障网络稳定运行的关键.文章研究一种基于主成分分析(Principal Component Analysis,PCA)和高斯混合模型(Gaussian Mixture Model,GMM)的宽带网络流量异常检测方法.首先,利用PCA技术对网络流量数据进行特征提取与降维处理,以降低数据的维度和复杂性;其次,采用GMM对降维后的数据进行分类;最后,使用KDD 99 数据集对所提方法进行测试.实验表明,该方法能够有效检测宽带网络中的异常流量,具有较高的适应性和稳定性.

外文标题：Detection Method of Broadband Network Traffic Anomaly Based on PCA and GMM

外文摘要：With the increasing scale and complexity of the network,anomaly detection of broadband network traffic has become the key to ensure the stable operation of the network.This paper studies an anomaly detection method of broadband network traffic based on Principal Component Analysis(PCA)and Gaussian Mixture Model(GMM).Firstly,PCA technology is used to extract features and reduce dimensions of network traffic data to reduce the dimension and complexity of data.Secondly,GMM is used to classify the data after dimension reduction;Finally,KDD 99 data set is used to test the proposed method.Experiments show that this method can effectively detect abnormal traffic in broadband networks,and has high adaptability and stability.

外文关键词：

Principal Component Analysis(PCA)Gaussian Mixture Model(GMM)network trafficanomaly detection

作者：

周永博

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作者单位：

郓城县第五人民医院,山东菏泽 274700

关键词：

主成分分析(PCA) 高斯混合模型(GMM) 网络流量异常检测

出版年：

2024

DOI：