dhcp-Technical-White-Paper

说明：  筛选出发生在TCP层的Dos流量和normal流量 `get_train_data.py` 其中Dos流量的标签`'back.', 'land.', 'neptune.','smurf.', 'teardrop.', 'pod.'` normal流量的标签`'normal.'` 生成文件`dos.kddcup.data.corrected.csv` * 通过统计特征进行数据筛选 `wrap_up.py` * 随机森林对特征重要性进行排序 折交叉验证(Filter out DOS traffic and normal traffic in TCP layer ` get_ train_ data.py ` The labels of DOS traffic are 'back', 'land', 'Neptune', 'Smurf', 'teardrop.'pod.'` Label of normal traffic ''normal.'` Generating files` dos.kddcup.data . corrected.csv ` *Data filtering through statistical features ` wrap_ up.py ` *Random forests rank the importance of features Fold cross validation)

RPEM算法在聚类分析中比ＥＭ算法更好效果。(RPEM algorithm in clustering analysis of better results than EM algorithm.)

AWAG经过ISDN拨叫一个号码并且传送一个消息。它还可以拨叫另外一个号码如果第一个号码不通或者没有人接听电话。-AWAG ISDN dial a number and send a message. It can also dial another number if a number of unreasonable or nobody answered the phone.

osCommerce最好的电子商务程序 2.2-osCommerce procedures for the best e-commerce 2.2

基于偏置场矫正的模糊C均值聚类算法的实现 经调试，可以运行(Bias field correction based on fuzzy C-means clustering algorithm to achieve through debugging, you can run)

一个流媒体的开发源代码C语言函数库，看了就知道。-a Streaming Media Development C language source code libraries, read on to know.

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输入是误差和误差变化率.

此程序是神经网络中基于梯度的径向基函数算法，在MATLAB中实现。用一个2－n－1结构的RBF网对SISO系统进行建模，网络的两个输入为u(k-1)和y(k-1)，输出为 y(k)。令y(0)=0，按飞线性系统产生200个样本，其中前100个样本用于训练，后100个样本用于测试。(This procedure is based on the gradient neural network radial basis function algorithm is implemented in MATLAB. With a 2-n-1 structure of RBF network model of the SISO system, the network of two input u (k-1) and y (k-1), output y (k). So that y (0) = 0, the linear system produced by flying 200 samples, of which the first 100 samples for training, after 100 samples for testing.)

说明：  深入学习Python介绍领域的深入学习使用Python语言和强大的Keras图书馆。通过keras创造者和谷歌人工智能研究者弗兰?ois Chollet写的，这本书让你了解通过直观的解释和实例。您将探索具有挑战性的概念和实践与计算机视觉，自然语言处理和生成模型的应用程序。当你完成学业的时候，你将具备在自己的项目中应用深度学习的知识和动手能力。(Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher Francois Chollet, this book builds your understanding through intuitive explanations and practical examples. You'll explore challenging concepts and practice with applications in computer vision, natural-language processing, and generative models. By the time you finish, you'll have the knowledge and hands-on skills to apply deep learning in your own projects.)