An optimization method for reasoning results is presented, such as recursive grey fitting model for single sequence and attribute correlativity model for multi-dimension data.
分别在单序列时建立递进灰拟合模型，在多维数据集时利用属性相关性，对插值结果进行学习优化。
They perform frequency, mean, minimum, maximum, and standard deviation analysis on attribute data and save the results in a new table.
这些工具完成对属性数据的频次、平均值、最小值、最大值和标准偏差的分析，并将结果存于一个新的表格中。
The experimental results indicate that the algorithm is very successful in picking different types of attacks in data set based on different attribute features.
实验表明该算法针对不同特征属性的数据集，检测不同类型的入侵行为，具有很好的检测结果。
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