資源簡介
基于Tensorflow用CNN（卷積神經網絡）處理kdd99數據集，代碼包括預處理代碼和分類代碼，準確率99.6%以上，并且快速收斂至最優值。
(Based on Tensorflow (convolutional neural network) processing KDD99 data set based on CNN, the code includes preprocessing code and classification code, the accuracy rate is more than 99.6%, and quickly converge to the optimal value.)
代碼片段和文件信息
#/usr/bin/python2.7
#coding:utf-8
from?__future__?import?print_function
import?tensorflow?as?tf
import?randomcsv
def?next_batch（feature_listlabel_listsize）:
????feature_batch_temp=[]
????label_batch_temp=[]
????f_list?=?random.sample（range（len（feature_list））?size）
????for?i?in?f_list:
????????feature_batch_temp.append（feature_list[i]）
????for?i?in?f_list:
????????label_batch_temp.append（label_list[i]）
????return?feature_batch_templabel_batch_temp
def?weight_variable（shapelayer_name）:
????#定義一個shape形狀的weights張量
????with?tf.name_scope（layer_name?+?‘_Weights‘）:
????????Weights?=?tf.Variable（tf.truncated_normal（shape?stddev=0.1）name=‘W‘）
????tf.histogram_summary（layer_name?+?‘_Weights‘?Weights）
????return?Weights
def?bias_variable（shapelayer_name）:
????#定義一個shape形狀的bias張量
????with?tf.name_scope（layer_name?+?‘_biases‘）:
????????biases?=?tf.Variable（tf.constant（0.1?shape=shape）name=‘b‘）
????tf.histogram_summary（layer_name?+?‘_biases‘?biases）
????return?biases
def?conv2d（x?Wlayer_name）:
????#卷積計算函數
????#?stride?[1?x步長?y步長?1]
????#?padding:SAME/FULL/VALID（邊距處理方式）
????with?tf.name_scope（layer_name?+?‘_h_conv2d‘）:
????????h_conv2d?=?tf.nn.conv2d（x?W?strides=[1?1?1?1]?padding=‘SAME‘）
????return?h_conv2d
def?max_pool_2x2（xlayer_name）:
????#?max池化函數
????#?ksize?[1?x邊長?y邊長1]?池化窗口大小
????#?stride?[1?x步長?y步長?1]
????#?padding:SAME/FULL/VALID（邊距處理方式）
????with?tf.name_scope（layer_name?+?‘_h_pool‘）:
????????h_pool?=?tf.nn.max_pool（x?ksize=[1221]?strides=[1221]?padding=‘SAME‘）
????return?h_pool
def?load_data（）:
????global?feature
????global?label
????global?feature_full
????global?label_full
????feature=[]
????label=[]
????feature_full=[]
????label_full=[]
????file_path?=‘/home/peter/Desktop/pycharm/ids-kdd99/kddcup.data_10_percent_corrected_handled2.cvs‘
????with?（open（file_path‘r‘））?as?data_from:
????????csv_reader=csv.reader（data_from）
????????for?i?in?csv_reader:
????????????#?print?i
????????????label_list=[0]*23
????????????feature.append（i[:36]）
????????????label_list[int（i[41]）]=1
????????????label.append（label_list）
????????????#?print?label
????????????#?print?feature
????file_path_full?=‘/home/peter/Desktop/pycharm/ids-kdd99/kddcup.data.corrected_handled2.cvs‘
????with?（open（file_path_full‘r‘））?as?data_from_full:
????????csv_reader_full=csv.reader（data_from_full）
????????for?j?in?csv_reader_full:
????????????#?print?i
????????????label_list_full=[0]*23
????????????feature_full.append（j[:36]）
????????????label_list_full[int（j[41]）]=1
????????????label_full.append（label_list_full）
if?__name__??==?‘__main__‘:
????global?feature
????global?label
????global?feature_full
????global?label_full
????#?load數據
????load_data（）
????feature_test?=?feature
????feature_train?=feature_full
????label_test?=?label
????label_test_full?=?label_full
????#?定義用以輸入的palceholder
????with?tf.name_scope（‘inputs‘）:
????????xs?=?tf.placeholder（tf.float32?[None?36]name=‘pic_data‘）?#?6x6
????????ys?=?tf.placeholder（tf.float32?[None?23] ?屬性????????????大小?????日期????時間???名稱
-----------?---------??----------?-----??----
?????目錄???????????0??2017-06-08?20:49??ids-kdd99\
?????目錄???????????0??2017-06-08?20:49??ids-kdd99\.idea\
?????文件?????????398??2016-12-27?15:54??ids-kdd99\.idea\ids-kdd99.iml
?????文件?????????682??2016-12-27?15:53??ids-kdd99\.idea\misc.xm
?????文件?????????270??2016-12-27?15:53??ids-kdd99\.idea\modules.xm
?????文件???????42708??2016-12-29?10:01??ids-kdd99\.idea\workspace.xm
?????文件????????6944??2016-12-29?16:58??ids-kdd99\cnn_main.py
?????文件????????2977??2016-12-29?16:55??ids-kdd99\handle2.py
?????文件????18115902??2016-12-29?09:58??ids-kdd99\kddcup.data.gz
?????文件?????2144903??2016-12-28?16:45??ids-kdd99\kddcup.data_10_percent.gz
?????文件????????4659??2016-12-29?17:00??ids-kdd99\main.py
?????文件????????6944??2017-02-27?15:12??ids-kdd99\mian_cnn.py
?????目錄???????????0??2017-06-08?20:49??ids-kdd99\multi_logs\
?????文件???????53246??2016-12-29?11:38??ids-kdd99\multi_logs\events.out.tfevents.1482980284.zjx-24000635
?????目錄???????????0??2017-06-08?20:49??ids-kdd99\multi_logs\test\
?????文件??????155823??2016-12-29?11:38??ids-kdd99\multi_logs\test\events.out.tfevents.1482980284.zjx-24000635
?????目錄???????????0??2017-06-08?20:49??ids-kdd99\multi_logs\train\
?????文件??????155823??2016-12-29?11:38??ids-kdd99\multi_logs\train\events.out.tfevents.1482980284.zjx-24000635
?????文件?????????328??2016-12-29?17:11??ids-kdd99\readMe.txt.txt
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