keras實現網路流量分類功能的BP神經網路
阿新 • • 發佈：2018-12-10
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資料集選用KDD99
資料下載地址：http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html
需求：https://blog.csdn.net/com_stu_zhang/article/details/6987632 -
執行環境
win10+keras
安裝步驟：https://blog.csdn.net/u010916338/article/details/83822562 -
資料預處理
包含數值替換文字、數值歸一化、標籤獨熱編碼
# -*- coding: utf-8 -*- """ Created on Tue Nov 6 09:24:20 2018 @author: hrh """ import pandas as pd from sklearn.preprocessing import OneHotEncoder from pandas.core.frame import DataFrame def get_total_data(): data = pd.read_csv('data_test.csv',header=None) data[1]=data[1].map({'tcp':0, 'udp':1, 'icmp':2}) data[2]=data[2].map({'aol':0, 'auth':1, 'bgp':2, 'courier':3, 'csnet_ns':4,'ctf':5, 'daytime':6, 'discard':7, 'domain':8, 'domain_u':9,'echo':10, 'eco_i':11, 'ecr_i':12, 'efs':13, 'exec':14,'finger':15, 'ftp':16, 'ftp_data':17, 'gopher':18, 'harvest':19,'hostnames':20, 'http':21, 'http_2784':22, 'http_443':23, 'http_8001':24,'imap4':25, 'IRC':26, 'iso_tsap':27, 'klogin':28, 'kshell':29,'ldap':30, 'link':31, 'login':32, 'mtp':33, 'name':34,'netbios_dgm':35, 'netbios_ns':36, 'netbios_ssn':37, 'netstat':38, 'nnsp':39,'nntp':40, 'ntp_u':41, 'other':42, 'pm_dump':43, 'pop_2':44,'pop_3':45, 'printer':46, 'private':47, 'red_i':48, 'remote_job':49,'rje':50, 'shell':51, 'smtp':52, 'sql_net':53, 'ssh':54,'sunrpc':55, 'supdup':56, 'systat':57, 'telnet':58, 'tftp_u':59,'tim_i':60, 'time':61, 'urh_i':62, 'urp_i':63, 'uucp':64,'uucp_path':65, 'vmnet':66, 'whois':67, 'X11':68, 'Z39_50':69}) data[3]=data[3].map({'OTH':0, 'REJ':0, 'RSTO':0,'RSTOS0':0, 'RSTR':0, 'S0':0,'S1':0, 'S2':0, 'S3':0,'SF':1, 'SH':0}) data[41]=data[41].map({'normal.':0, 'ipsweep.':1, 'mscan.':2, 'nmap.':3, 'portsweep.':4, 'saint.':5, 'satan.':6, 'apache2.':7,'back.':8, 'land.':9, 'mailbomb.':10, 'neptune.':11, 'pod.':12,'processtable.':13, 'smurf.':14, 'teardrop.':15, 'udpstorm.':16, 'buffer_overflow.':17, 'httptunnel.':18, 'loadmodule.':19, 'perl.':20, 'ps.':21,'rootkit.':22, 'sqlattack.':23, 'xterm.':24, 'ftp_write.':25,'guess_passwd.':26, 'imap.':27, 'multihop.':28, 'named.':29, 'phf.':30,'sendmail.':31, 'snmpgetattack.':32, 'snmpguess.':33, 'spy.':34, 'warezclient.':35,'warezmaster.':36, 'worm.':37, 'xlock.':38, 'xsnoop.':39}) data[2] = (data[2]-data[2].min())/(data[2].max() - data[2].min()) data[4] = (data[4]-data[4].min())/(data[4].max() - data[4].min()) data[5] = (data[5]-data[5].min())/(data[5].max() - data[5].min()) data[22] = (data[22]-data[22].min())/(data[22].max() - data[22].min()) data[23] = (data[23]-data[23].min())/(data[23].max() - data[23].min()) data[31] = (data[31]-data[31].min())/(data[31].max() - data[31].min()) data[32] = (data[32]-data[32].min())/(data[32].max() - data[32].min()) return data def get_target_data(): data = get_total_data() enc = OneHotEncoder(sparse = False) enc.fit([[0], [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [32], [33], [34], [35], [36], [37], [38], [39]]) result = enc.transform(data[[41]]) return DataFrame(result) def get_input_data(): data = get_total_data() del data[41] return data if __name__ == '__main__': data_input = get_input_data() # data = get_total_data() data_input.to_csv('data_test_input.csv',header=None,index=None) data_target = get_target_data() data_target.to_csv('data_test_target.csv',index=None,header=None)
- 程式碼
# -*- coding: utf-8 -*- """ Created on Mon Nov 5 16:34:42 2018 @author: hrh """ import time start = time.time() from sklearn.preprocessing import OneHotEncoder import tensorflow as tf from keras.models import Sequential from keras.layers.core import Dense, Activation import pandas as pd from keras.optimizers import SGD if __name__ == '__main__': input_data = pd.read_csv('data_input.csv',header=None) target_data = pd.read_csv('data_target.csv',header=None) input_data_test = pd.read_csv('data_test_input.csv',header=None) target_data_test = pd.read_csv('data_test_target.csv',header=None) model = Sequential() #層次模型 model.add(Dense(54, input_dim=41, init='uniform', activation='relu')) # model.add(Dense(64, input_dim=54, init='uniform', activation='relu')) model.add(Dense(40, init='uniform', activation='relu')) model.add(Dense(40, init='uniform', activation='softmax')) sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True) model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy']) model.fit(input_data, target_data, nb_epoch=12, batch_size=128) # scores = model.evaluate(input_data, target_data) # print("%s: %.2f%%" % (model.metrics_names[1], scores[1]*10)) # 將測試集輸入到訓練好的模型中，檢視測試集的誤差 loss_and_metrics = model.evaluate(input_data_test, target_data_test, batch_size=128) result = model.predict(input_data_test, batch_size=128) print('測試集的預測結果為：', result) stop = time.time() print(str(stop-start) + "秒")
- 執行結果
Epoch 1/12 494021/494021 [==============================] - 5s 10us/step - loss: 0.1277 - acc: 0.9720 Epoch 2/12 494021/494021 [==============================] - 4s 9us/step - loss: 0.0327 - acc: 0.9946 Epoch 3/12 494021/494021 [==============================] - 4s 9us/step - loss: 0.0252 - acc: 0.9954 Epoch 4/12 494021/494021 [==============================] - 4s 9us/step - loss: 0.0215 - acc: 0.9963 Epoch 5/12 494021/494021 [==============================] - 4s 9us/step - loss: 0.0192 - acc: 0.9967 Epoch 6/12 494021/494021 [==============================] - 4s 9us/step - loss: 0.0177 - acc: 0.9971 Epoch 7/12 494021/494021 [==============================] - 4s 9us/step - loss: 0.0164 - acc: 0.9973 Epoch 8/12 494021/494021 [==============================] - 4s 9us/step - loss: 0.0155 - acc: 0.9976 Epoch 9/12 494021/494021 [==============================] - 4s 9us/step - loss: 0.0149 - acc: 0.9977 Epoch 10/12 494021/494021 [==============================] - 4s 9us/step - loss: 0.0142 - acc: 0.9978 Epoch 11/12 494021/494021 [==============================] - 4s 9us/step - loss: 0.0138 - acc: 0.9979 Epoch 12/12 494021/494021 [==============================] - 4s 9us/step - loss: 0.0134 - acc: 0.9979 311029/311029 [==============================] - 1s 4us/step Test loss: nan Test accuracy: 0.9157281153902965 測試集的預測結果為： [[9.9969363e-01 6.1610535e-07 7.2848221e-11 ... 7.0689246e-11 1.6535544e-10 2.0521278e-10] [9.9969363e-01 6.1610535e-07 7.2848221e-11 ... 7.0689246e-11 1.6535544e-10 2.0521278e-10] [9.9969363e-01 6.1610535e-07 7.2848221e-11 ... 7.0689246e-11 1.6535544e-10 2.0521278e-10] ... [9.9966860e-01 6.4044093e-07 7.9545072e-11 ... 7.7094046e-11 1.8038426e-10 2.2377748e-10] [9.9966013e-01 6.5163550e-07 7.8919801e-11 ... 7.6405368e-11 1.7924477e-10 2.2227653e-10] [9.9966860e-01 6.4044093e-07 7.9545072e-11 ... 7.7094046e-11 1.8038426e-10 2.2377748e-10]] 61.35442543029785秒