文件名称：kdd99-scikit：scikit-learn通过决策树和神经网络解决kdd99数据集的问题
文件大小：5.42MB
文件格式：ZIP
更新时间：2024-02-23 22:13:06
scikit-learn intrusion-detection mlp confusion-matrix decision-tree
kdd99-scikit scikit-learn使用决策树（CART）和多层感知器解决kdd99数据集的解决方案 Kdd99数据集简介 是建立一个网络入侵检测器，这是一种能够区分“不良”连接（称为入侵或攻击）和“良好”正常连接的预测模型。 请注意，测试数据并非与训练数据具有相同的概率分布，并且包括不在训练数据中的特定攻击类型。 训练数据快照（ raw/kddcup.data_10_percent.txt ）： 0,tcp,http,SF,181,5450,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,8,8,0.00,0.00,0.00,0.00,1.00,0.00,0.00,9,9,1.00,0.00,0.11,0.00,0.00,0.00,0.00,0.00,normal. 0,tcp,http,SF,239,486,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,8,8,0.00,0.00,0.00,0.00,1.00,0.00,0.00,19,19,1.00,0.00,0.05,0.00,0.00,0.00,0.00,0.00,norm
【文件预览】：
kdd99-scikit-master
----MLP()
--------MLP_Trainer.py(3KB)
--------MLP_Predictor.pyc(1000B)
--------output()
--------__init__.py(0B)
--------MLP_Predictor.py(439B)
--------MLP_Trainer.pyc(4KB)
--------MLP_Runner.py(693B)
----__init__.pyc(161B)
----Variable.py(2KB)
----raw()
--------corrected.txt(7.28MB)
--------kddcup.data_10_percent.txt(71.42MB)
--------testdata_unlabeled_50000.txt(7.2MB)
--------training_attack_types.txt(280B)
----CART()
--------output()
--------__init__.py(0B)
--------CART_Predictor.py(1KB)
--------CART_all.py(5KB)
--------CART_Trainer.pyc(5KB)
--------CART_Trainer.py(4KB)
--------CART_Runner.py(2KB)
--------CART_Predictor.pyc(1KB)
----data()
--------corrected.txt(7.03MB)
--------.DS_Store(6KB)
--------kddcup.data_10_percent.txt(68.75MB)
----__init__.py(0B)
----Snip20161130_3.png(398KB)
----Variable.pyc(2KB)
----Mongo_Con.pyc(4KB)
----Preprocessing.py(1KB)
----README.md(6KB)
----Preprocessing_all.py(5KB)
----Preprocessing.pyc(2KB)
----Mongo_Con.py(4KB)