基于数据挖掘的网络状态异常检测

Anomaly Detection of Network State Based on Data Mining

针对目前网络状态异常行为检测正确率低的问题,提出一种基于数据挖掘的网络状态异常检测模型.首先提取网络状态信号,通过小波变换对信号进行预处理,并提取网络状态异常检测的特征;然后通过回声状态网络对网络状态异常检测进行建模,并通过遗传算法对回声状态网络的参数进行优化;最后采用网络状态异常数据集对模型的有效性进行测试.测试结果表明,数据挖掘技术可以准确检测各种网络状态异常行为.

Aiming at the problem of low detection accuracy for abnormal behavior of network states,we proposed an anomaly detection model of network state based on data mining.Firstly,the network state signal was extracted,and the signal was pretreated by wavelet transform,and the features of the network anomaly detection were extracted.Secondly,the network state anomaly detection model was built by echo state network,and genetic algorithm was used to optimize the parameters of the echo state network.Finally,the network state anomaly data sets were used to test the effectiveness of the model.The test results show that data mining technology can accurately detect abnormal behavior of various network states.