KDD99入侵检测数据预处理和分类源代码及数据集.zip

import tensorflow as tf import random import csv def next_batch(feature_list,label_list,size): 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_temp,label_batch_temp def weight_variable(shape): #定义一个shape形状的weights张量 Weights = tf.Variable(tf.truncated_normal(shape, stddev=0.1),name='W') return Weights def bias_variable(shape): #定义一个shape形状的bias张量 biases = tf.Variable(tf.constant(0.1, shape=shape),name='b') return biases def conv2d(x, W): #卷积计算函数 # stride [1, x步长, y步长, 1] # padding:SAME/FULL/VALID（边距处理方式） h_conv2d = tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME') return h_conv2d def max_pool_2x2(x): # max池化函数 # ksize [1, x边长, y边长,1] 池化窗口大小 # stride [1, x步长, y步长, 1] # padding:SAME/FULL/VALID（边距处理方式） h_pool = tf.nn.max_pool(x, ksize=[1,2,2,1], strides=[1,2,2,1], 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 ='kddcup.data_10_percent_corrected.csv' with (open(file_path,'r')) as data_from: csv_reader=csv.reader(data_from) for rowi in csv_reader: label_list=[0]*23 feature.append(rowi[:36]) label_list[int(rowi[41])]=1 label.append(label_list) file_path_full ='kddcup.data_10_percent_corrected.csv' with (open(file_path_full,'r')) as data_from_full: csv_reader_full=csv.reader(data_from_full) for rowj in csv_reader_full: label_list_full=[0]*23 feature_full.append(rowj[:36]) label_list_full[int(rowj[41])]=1 label_full.append(label_list_full) if __name__ == '__main__': global feature global label global feature_full global label_full # load数据 load_data() feature_test = feature feature_train =feature_full label_test = label label_train= label_full # 定义用以输入的palceholder xs = tf.placeholder(tf.float32, [None, 36]) # 6x6 ys = tf.placeholder(tf.float32, [None, 23]) keep_prob = tf.placeholder(tf.float32) x_image = tf.reshape(xs, [-1, 6, 6, 1]) # -1表示不约束这个位置 1表示信道1（灰度图仅有一个信道） ## 第一个卷积层 ## W_conv1 = weight_variable([3,3,1,32]) # 卷积窗 3x3, 输入厚度 1, 输出厚度 32 b_conv1 = bias_variable([32]) h_conv1 = tf.nn.sigmoid(conv2d(x_image,W_conv1) + b_conv1) # 输出大小： 6x6x32 h_pool1 = max_pool_2x2(h_conv1) # 输出大小： 3x3x32 ## 第一个全连接层 ## # 带有dropout W_fc1 = weight_variable([3*3*32,1024]) b_fc1 = bias_variable([1024]) h_pool2_flat = tf.reshape(h_pool1, [-1,3*3*32]) h_fc1 = tf.nn.sigmoid(tf.matmul(h_pool2_flat, W_fc1) + b_fc1) h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob) ## 第二个全连接层 ## W_fc2 = weight_variable([1024, 23]) b_fc2 = bias_variable([23]) prediction = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2) # 计算loss/cost cross_entropy = -tf.reduce_sum(ys * tf.log(prediction)) # loss # 计算accuracy correct_prediction = tf.equal(tf.argmax(prediction,1), tf.argmax(ys,1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) # 使用Adam优化器来实现梯度最速下降 train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy) with tf.Session() as sess: # 初始化所有张量 sess.run(tf.global_variables_initializer()) for step in range(501): feature_train_batch, label_train_batch = next_batch(feature_train, label_train,1000) # 随机梯度下降训练，每次选大小为1000的batch feature_test_batch, label_test_batch = next_batch(feature_test, label_test,1000) # 随机梯度下降训练，每次选大小为1000的batch sess.run(train_step, feed_dict={xs: feature_train_batch, ys: label_train_batch, keep_prob: 0.5}) if step % 50 == 0: print(step, sess.run(tf.argmax(prediction, 1)[7:27], feed_dict={xs: feature_test, ys: label_test, keep_prob: 1}), sess.run(tf.argmax(ys, 1)[7:27], feed_dict={xs: feature_test, ys: label_test, keep_prob: 1}), sess.run(accuracy, feed_dict={xs: feature_test, ys: label_test, keep_prob: 1}), sess.run(accuracy, feed_dict={xs: feature_train_batch, ys: label_train_batch, keep_prob: 1}))