针对网络异常检测虚警率偏高的问题，提出了一种基于属性相似度云模型的网络异常检测新方法。

A new method for the anomaly detection based on the attributes similarity and the cloud model was proposed to alleviate the high false positive rate problem in the detection.

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实验结果表明，基于相对熵理论的多测度网络异常检测方法对于多种攻击的平均检测率达到83.5%。

Experimental result shows that the average detection rate of RETMMAD can reach to 83.5%. Considering for practicability of the RETMMAD, arriving at a decision for the threshold is often difficult.

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本文描述了一个基于相关特征矩阵和神经网络的异常检测方法。

This article presents a anomaly detection method based on correlation eigen matrix and neural network.

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