利用基于网络实时数据来建立测量标准，从而窃取用户的情绪状况及喜好，这主意并没有多少新意，即使用它来预测人的行为也是一样。
The idea of tapping web-based data to build a real-time measure of users' emotions and preferences is not new. Nor is that of using the results to predict their behaviour.
建立一个可扩展的、通用的网络测量平台是全面了解大规模网络行为的基础。
Building a scalable and universal network measurement platform is the basis to understand synthetical behaviors of large-scale networks.
传统的业务流模型大多是基于泊松或贝努力过程的，而这些模型表现出的行为与实际网络的测量结果不符。
The conventional models are mostly based on Poisson model or Bernoulli process, but the activity of these models is not consistent with the measured results of the real networks.
网络性能测量是网络行为分析的基础。
The network function measurement is an analytical basis of network behavior.
基于大规模网络流量的统计特征，寻找能够评价网络行为的稳定测度，并建立抽样测量模型。
Based on statistics character of traffic in a large-scale network, the steady metrics that can estimated network behavior are found and a sampling measurement model is presented in this paper.
先将数据集用于神经网络的训练，然后使用训练后的RNN对网络数据进行孤立度测量，根据度量结果判定是否为入侵行为。
First train RNN with datasets, then use trained RNN to provide the measurement of the outlyingness of data records. The performance of the RNNs is assessed by using a ranked score measurement.
先将数据集用于神经网络的训练，然后使用训练后的RNN对网络数据进行孤立度测量，根据度量结果判定是否为入侵行为。
First train RNN with datasets, then use trained RNN to provide the measurement of the outlyingness of data records. The performance of the RNNs is assessed by using a ranked score measurement.
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