The main problem of existing static vulnerability detection methods based source code analysis is their high false positive and false negative rates.

现有的采用基于源代码分析的漏洞静态检测方法中存在的主要问题是误报率和漏报率较高。

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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.

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。

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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.

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。

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