【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.更多还原

【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.更多还原