A Network Traffic Anomaly Detection Method Based on Non-parametric Statistical Theory

DING Fan; YANG Yue; LI Jun

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A Network Traffic Anomaly Detection Method Based on Non-parametric Statistical Theory

更新时间：2026-09-04

- A Network Traffic Anomaly Detection Method Based on Non-parametric Statistical Theory
- Vol. 28, Issue 11, Pages: 23-26(2011)
- 作者机构：
  
  华中科技大学图像识别与人工智能研究所,湖北,武汉,430074
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2011
- 稿件说明：
移动端阅览
丁帆, 杨越, 李军. 一种基于非参数统计理论的网络流量异常检测方法[J]. 微电子学与计算机, 2011, 28(11): 23-26.

DING Fan, YANG Yue, LI Jun. A Network Traffic Anomaly Detection Method Based on Non-parametric Statistical Theory[J]. 2011, 28(11): 23-26.
丁帆, 杨越, 李军. 一种基于非参数统计理论的网络流量异常检测方法[J]. 微电子学与计算机, 2011, 28(11): 23-26. DOI：

DING Fan, YANG Yue, LI Jun. A Network Traffic Anomaly Detection Method Based on Non-parametric Statistical Theory[J]. 2011, 28(11): 23-26. DOI：

摘要

提出了一种新的基于非参数高斯核函数分布的网络流量异常检测方法.与目前核函数应用于分类、神经网络、机器学习的方法和原理均不同

针对异常发生时流量出现的扰动

使用能显著反映流量形状变化的核带宽作为特征统计量

进行网络流量分析.实验结果表明

该方法能显著降低计算复杂度和误检率

提高检测率.

Abstract

A new network traffic anomaly detection method based on non-parametric statistics of Gaussian kernel function distribution has been proposed in this paper.In addition

the method is different from the current theory and principle of kernel function applications

such as classification

neural network

machine learning and so on.Considering the fluctuation of the network traffic when anomaly occurs

this paper uses the bandwidth of kernel function as the feature value which can significantly reflect the change of network traffic to analyze the network traffic.Compared with other methods

experimental results show that this method can significantly reduce the computational complexity and false detection rate

also improve the detection rate.

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