├── 1-FeatureExtraction.py
├── 2-Training_Data_Generating.py
├── 3-Action_Sequence_Training.py
├── 4-Static_Feature_Training.py
├── 5-Role-Feature_and_Mix.py
├── 6-Mix_data_for_MLP.py
├── 7-MLP_Training_and_Metrics.py
├── Data
    ├── EDB0714
    │   ├── Mix
    │   │   ├── Mix_loss.csv
    │   │   └── label_loss.csv
    │   ├── Model
    │   │   ├── Action
    │   │   │   └── model.h5
    │   │   ├── Feature
    │   │   │   └── model.h5
    │   │   ├── Role_features.jpg
    │   │   └── Role_loss.csv
    │   ├── device.csv
    │   ├── email.csv
    │   ├── feature
    │   │   ├── data_all.csv
    │   │   ├── data_out.csv
    │   │   ├── data_test.csv
    │   │   ├── data_train.csv
    │   │   ├── label_all.csv
    │   │   ├── label_test.csv
    │   │   ├── label_train.csv
    │   │   ├── loss_all.csv
    │   │   ├── loss_all.jpg
    │   │   ├── myloss_all.csv
    │   │   ├── myloss_all.jpg
    │   │   ├── predict.csv
    │   │   └── predict_all.csv
    │   ├── file.csv
    │   ├── http.csv
    │   ├── logon.csv
    │   ├── new
    │   │   ├── device.csv
    │   │   ├── device2.csv
    │   │   ├── email.csv
    │   │   ├── email2.csv
    │   │   ├── file.csv
    │   │   ├── file2.csv
    │   │   ├── http.csv
    │   │   ├── http2.csv
    │   │   ├── logon.csv
    │   │   └── logon2.csv
    │   ├── psychometric.csv
    │   └── sequence
    │   │   ├── actions_sequence.csv
    │   │   ├── data_all.csv
    │   │   ├── data_test.csv
    │   │   ├── data_train.csv
    │   │   ├── label_all.csv
    │   │   ├── label_test.csv
    │   │   ├── label_train.csv
    │   │   ├── loss_all.csv
    │   │   ├── loss_all.jpg
    │   │   ├── predict.csv
    │   │   ├── sequence_code.csv
    │   │   └── sequence_temp.csv
    ├── HXL0968
    │   ├── Mix
    │   │   ├── Mix_loss.csv
    │   │   └── label_loss.csv
    │   ├── Model
    │   │   ├── Action
    │   │   │   └── model.h5
    │   │   ├── Feature
    │   │   │   └── model.h5
    │   │   ├── Role_features.jpg
    │   │   └── Role_loss.csv
    │   ├── device.csv
    │   ├── email.csv
    │   ├── feature
    │   │   ├── data_all.csv
    │   │   ├── data_out.csv
    │   │   ├── data_test.csv
    │   │   ├── data_train.csv
    │   │   ├── label_all.csv
    │   │   ├── label_test.csv
    │   │   ├── label_train.csv
    │   │   ├── loss_all.csv
    │   │   ├── loss_all.jpg
    │   │   ├── myloss_all.csv
    │   │   ├── myloss_all.jpg
    │   │   ├── predict.csv
    │   │   └── predict_all.csv
    │   ├── file.csv
    │   ├── http.csv
    │   ├── logon.csv
    │   ├── new
    │   │   ├── device.csv
    │   │   ├── device2.csv
    │   │   ├── email.csv
    │   │   ├── email2.csv
    │   │   ├── file.csv
    │   │   ├── file2.csv
    │   │   ├── http.csv
    │   │   ├── http2.csv
    │   │   ├── logon.csv
    │   │   └── logon2.csv
    │   ├── psychometric.csv
    │   └── sequence
    │   │   ├── actions_sequence.csv
    │   │   ├── data_all.csv
    │   │   ├── data_test.csv
    │   │   ├── data_train.csv
    │   │   ├── label_all.csv
    │   │   ├── label_test.csv
    │   │   ├── label_train.csv
    │   │   ├── loss_all.csv
    │   │   ├── loss_all.jpg
    │   │   ├── predict.csv
    │   │   ├── sequence_code.csv
    │   │   └── sequence_temp.csv
    ├── Mix
    │   ├── Disorder_label.csv
    │   ├── Disorder_loss.csv
    │   ├── MLP.h5
    │   ├── Mix_all_label.csv
    │   ├── Mix_all_loss.csv
    │   ├── ROC.jpg
    │   ├── label_test.csv
    │   ├── label_train.csv
    │   ├── predict.csv
    │   ├── predict_label.csv
    │   ├── test.csv
    │   └── train.csv
    ├── Role_feature.csv
    ├── TNM0961
    │   ├── Mix
    │   │   ├── Mix_loss.csv
    │   │   └── label_loss.csv
    │   ├── Model
    │   │   ├── Action
    │   │   │   └── model.h5
    │   │   ├── Feature
    │   │   │   └── model.h5
    │   │   ├── Role_features.jpg
    │   │   └── Role_loss.csv
    │   ├── device.csv
    │   ├── email.csv
    │   ├── feature
    │   │   ├── data_all.csv
    │   │   ├── data_out.csv
    │   │   ├── data_test.csv
    │   │   ├── data_train.csv
    │   │   ├── label_all.csv
    │   │   ├── label_test.csv
    │   │   ├── label_train.csv
    │   │   ├── loss_all.csv
    │   │   ├── loss_all.jpg
    │   │   ├── myloss_all.csv
    │   │   ├── myloss_all.jpg
    │   │   ├── predict.csv
    │   │   └── predict_all.csv
    │   ├── file.csv
    │   ├── http.csv
    │   ├── logon.csv
    │   ├── new
    │   │   ├── device.csv
    │   │   ├── device2.csv
    │   │   ├── email.csv
    │   │   ├── email2.csv
    │   │   ├── file.csv
    │   │   ├── file2.csv
    │   │   ├── http.csv
    │   │   ├── http2.csv
    │   │   ├── logon.csv
    │   │   └── logon2.csv
    │   ├── psychometric.csv
    │   └── sequence
    │   │   ├── actions_sequence.csv
    │   │   ├── data_all.csv
    │   │   ├── data_test.csv
    │   │   ├── data_train.csv
    │   │   ├── label_all.csv
    │   │   ├── label_test.csv
    │   │   ├── label_train.csv
    │   │   ├── loss_all.csv
    │   │   ├── loss_all.jpg
    │   │   ├── predict.csv
    │   │   ├── sequence_code.csv
    │   │   └── sequence_temp.csv
    └── readme.txt
├── LICENSE
├── README.md
└── __pycache__
    └── FeatureExtract.cpython-36.pyc


/2-Training_Data_Generating.py:
--------------------------------------------------------------------------------
  1 | import numpy as np 
  2 | import linecache
  3 | import os
  4 | # from FeatureExtract import path_check
  5 | 
  6 | 
  7 | # --------------------------------------- STEP THREE
  8 | 
  9 | def path_check(path):
 10 |     """ Check the path
 11 |     """
 12 |     folders=os.path.exists(path)
 13 |     if not folders:
 14 |         print("Create new folders: ' %s ' successfully!"%(path))
 15 |         os.makedirs(path)
 16 |     else:
 17 |         print("The folder  ' %s ' exits, Some data will be rewrited !"%(path))
 18 |         pass 
 19 |         # os._exit(0)
 20 | 
 21 | def count_line(files_in):
 22 |     file_open=open(files_in,'r')
 23 |     count=0
 24 |     for line in file_open:
 25 |         count+=1
 26 |     return count   
 27 | 
 28 | def data_generator(files_name,num_days=5):
 29 |     # 删除最后一行数据，因为可能会有缺漏 
 30 |     counts=count_line(files_name)-1
 31 |     real_counts=counts-num_days+1
 32 |     print (real_counts)   
 33 |     for i in range(real_counts):
 34 |         data_index=i+1
 35 |         label_index=data_index+4
 36 |         data_one=np.array(linecache.getline(files_name,data_index).strip().split(',')+linecache.getline(files_name,data_index+1).strip().split(',')+linecache.getline(files_name,data_index+2).strip().split(',')+linecache.getline(files_name,data_index+3).strip().split(','))
 37 |         labe_data=np.array(linecache.getline(files_name,label_index).strip().split(','))
 38 |         # print(np.shape(labe_data))
 39 |         # print (data_one)
 40 |         # exit (0)
 41 |         yield (data_one,labe_data)
 42 | 
 43 | 
 44 | 
 45 | # # types='ActionSequence'
 46 | # types='FeatureMap'
 47 | def type_check():
 48 |     if types=='FeatureMap':
 49 |     # -------- file_name for feture map
 50 |         files_name='Data/'+USERNAME+'/feature/data_out.csv'
 51 |         data_all='Data/'+USERNAME+'/feature/data_all.csv'
 52 |         label_all='Data/'+USERNAME+'/feature/label_all.csv'
 53 |         data_train='Data/'+USERNAME+'/feature/data_train.csv'
 54 |         data_test='Data/'+USERNAME+'/feature/data_test.csv'
 55 |         label_train='Data/'+USERNAME+'/feature/label_train.csv'
 56 |         label_test='Data/'+USERNAME+'/feature/label_test.csv'
 57 |     # -------- file_name for action sequence 
 58 |     else:
 59 |         files_name='Data/'+USERNAME+'/sequence/sequence_code.csv'
 60 |         data_all='Data/'+USERNAME+'/sequence/data_all.csv'
 61 |         label_all='Data/'+USERNAME+'/sequence/label_all.csv'
 62 |         data_train='Data/'+USERNAME+'/sequence/data_train.csv'
 63 |         data_test='Data/'+USERNAME+'/sequence/data_test.csv'
 64 |         label_train='Data/'+USERNAME+'/sequence/label_train.csv'
 65 |         label_test='Data/'+USERNAME+'/sequence/label_test.csv'
 66 | 
 67 | 
 68 | # # step one ------------------------------------- generate data with several days
 69 | # 数据先清空 (emtpy the data )
 70 | def data_clean(data_all,label_all):
 71 |     data_save=open(data_all,'wt')
 72 |     label_save=open(label_all,'wt')
 73 |     data_save.close()
 74 |     label_save.close()
 75 | # 数据保存 (save the data )
 76 | def generate_data(data_all,label_all,files_name):
 77 |     '''
 78 |     we extract every five data for our datatset,
 79 |     the former four data are used for input data, the fivth data are used as label. 
 80 |     '''
 81 |     data_save=open(data_all,'a+')
 82 |     label_save=open(label_all,'a+')
 83 |     batch_size=10
 84 |     num_days_for_OneSequence=5
 85 |     # 删除最后一行数据，因为可能会有缺漏 
 86 |     counts=count_line(files_name)-1  # the number of data
 87 |     real_counts=counts-num_days_for_OneSequence+1   
 88 |     rounds=int(real_counts/batch_size)
 89 |     print (rounds)
 90 |     for i in range(rounds):
 91 |         i=i*10
 92 |         data_batch=[]
 93 |         label_batch=[]
 94 |         for j in range (batch_size):
 95 |             data_index=i+j+1
 96 |             label_index=data_index+4
 97 |             data_one=linecache.getline(files_name,data_index).strip().split(',')+linecache.getline(files_name,data_index+1).strip().split(',')+linecache.getline(files_name,data_index+2).strip().split(',')+linecache.getline(files_name,data_index+3).strip().split(',')
 98 |             labe_data=linecache.getline(files_name,label_index).strip().split(',')
 99 |             data_batch.append(data_one)
100 |             label_batch.append(labe_data)
101 |            
102 |         data_batch=np.array(data_batch)
103 |         label_batch=np.array(label_batch)
104 |         
105 |         data_batch2=data_batch.astype('float32')
106 |         label_batch2=label_batch.astype('float32')
107 |         
108 |         # print (data_batch[0])
109 |         # exit(0)
110 |         np.savetxt(data_save,data_batch2,fmt='%f',delimiter=',')
111 |         np.savetxt(label_save,label_batch2,fmt='%f',delimiter=',')
112 | 
113 |     data_save.close()
114 |     label_save.close()
115 |     
116 | # step two----------------------------------------------- 划分 train and test ---------------------------------------------
117 | def train_test_generate(data_all,label_all,train_data_save,train_label_save,test_data_save,test_label_save,rate=0.8):
118 |     '''
119 |     we extract 'rate' (the defaults is 0.8) percent data for training, and the rest for testing. 
120 |     
121 |     '''
122 |     
123 |     data_in=open(data_all,'r')
124 |     label_in=open(label_all,'r')
125 |     data_train=open(train_data_save,'wt')
126 |     data_test=open(test_data_save,'wt')
127 |     label_train=open(train_label_save,'wt')
128 |     label_test=open(test_label_save,'wt')
129 |     data_num=count_line(data_all)
130 |     train_num=data_num*rate
131 |     # print(train_num)
132 |     index=0
133 |     for line in data_in:
134 |         if index<train_num:
135 |             data_train.writelines(line)
136 |         else:
137 |             data_test.writelines(line)
138 |         index=index+1
139 | 
140 |     data_train.close()
141 |     data_test.close()
142 | 
143 |     index=0
144 |     for label in label_in:
145 |         if index<train_num:
146 |             label_train.writelines(label)
147 |         else:
148 |             label_test.writelines(label)
149 |         index=index+1
150 |     label_train.close()
151 |     label_test.close()
152 |     data_in.close()
153 |     label_in.close()
154 | 
155 | 
156 | 
157 | 
158 | if __name__ == "__main__":
159 | 
160 |     USERNAME=''
161 |     # ---------- run change the types below and run again ------
162 |     # types='ActionSequence'
163 |     types='FeatureMap' 
164 | 
165 |     # ----------- generate data for every user  ----------------
166 |     user_sets=['EDB0714','TNM0961','HXL0968'] # 29 32 33
167 |     for username in user_sets:
168 |         USERNAME=username
169 |         if types=='FeatureMap':
170 |             # -------- file_name for feture map
171 |             files_name='Data/'+USERNAME+'/feature/data_out.csv'
172 |             data_all='Data/'+USERNAME+'/feature/data_all.csv'
173 |             label_all='Data/'+USERNAME+'/feature/label_all.csv'
174 |             data_train='Data/'+USERNAME+'/feature/data_train.csv'
175 |             data_test='Data/'+USERNAME+'/feature/data_test.csv'
176 |             label_train='Data/'+USERNAME+'/feature/label_train.csv'
177 |             label_test='Data/'+USERNAME+'/feature/label_test.csv'
178 |             # -------- file_name for action sequence 
179 |         elif types=='ActionSequence' :
180 |             files_name='Data/'+USERNAME+'/sequence/sequence_code.csv'
181 |             data_all='Data/'+USERNAME+'/sequence/data_all.csv'
182 |             label_all='Data/'+USERNAME+'/sequence/label_all.csv'
183 |             data_train='Data/'+USERNAME+'/sequence/data_train.csv'
184 |             data_test='Data/'+USERNAME+'/sequence/data_test.csv'
185 |             label_train='Data/'+USERNAME+'/sequence/label_train.csv'
186 |             label_test='Data/'+USERNAME+'/sequence/label_test.csv'   
187 |         else :
188 |             exit('Wrong type! Please check the settings!')
189 | 
190 | 
191 |         data_clean(data_all,label_all)
192 |         # generate the data for deep learning model
193 |         generate_data(data_all,label_all,files_name)
194 |         # extract training data and test data
195 |         train_test_generate(data_all,label_all,data_train,label_train,data_test,label_test,rate=0.7)
196 | 
197 | 
198 |                 


--------------------------------------------------------------------------------
/3-Action_Sequence_Training.py:
--------------------------------------------------------------------------------
  1 | # -*- coding: UTF-8 -*-
  2 | from keras.models import Sequential,load_model,Model
  3 | from keras.layers import Dense, Activation,Embedding,Conv2D,MaxPooling2D,Reshape,BatchNormalization,Dropout,Input,concatenate,GlobalAveragePooling2D,Flatten,ConvLSTM2D,ConvLSTM2DCell,LSTM,Conv3D
  4 | from keras.optimizers import adam
  5 | import numpy as np 
  6 | import linecache
  7 | import os
  8 | import tensorflow as tf 
  9 | from keras import losses
 10 | from keras import losses,metrics
 11 | from keras.callbacks import TensorBoard, ModelCheckpoint
 12 | import matplotlib.pyplot as plt
 13 | import warnings 
 14 | 
 15 | 
 16 | 
 17 | # losses.mean_squared_error
 18 | def path_check(path):
 19 |     '''
 20 |     Check the input path
 21 |     '''
 22 |     folders=os.path.exists(path)
 23 |     if not folders:
 24 |         print("Create new folders: ' %s ' successfully!"%(path))
 25 |         os.makedirs(path)
 26 |     else:
 27 |         print("The folder  ' %s ' exits, Please check!"%(path))
 28 |         pass
 29 |         # os._exit(0)
 30 | 
 31 | # -----------------------------------------------
 32 | 
 33 | def train(files_train,labels_train,save_path,files_test,predict_save,action_length):
 34 |     """ Deep Learning model for users' actions
 35 |     # Params:
 36 |     ----
 37 |         files_train: training data (data for model training)
 38 |         labels_train: labels of training data
 39 |         save_path: the path of model that is going to be saved 
 40 |         files_test: test data 
 41 |         predict_save: the predicted labels of test data 
 42 |         action_length: the length of input data (include both training data and test data)
 43 |     """
 44 |     seq_length=4
 45 |     input_dims=seq_length*action_length
 46 |     # ---------------模型输入 (Model input)---------------
 47 |     x_train=np.loadtxt(files_train,delimiter=',')
 48 |     y_train=np.loadtxt(labels_train,delimiter=',')
 49 | 
 50 |     # ---------------模型结构 (model structure)--------------
 51 |     main_input=Input(shape=(input_dims,),dtype='float32',name='MainInput')
 52 |     Inputs=Reshape((seq_length,action_length))(main_input)
 53 |     print(Inputs)
 54 |     layer=LSTM(100,return_sequences=True,activation='tanh')(Inputs)
 55 |     layer=LSTM(160,return_sequences=False,activation='tanh')(layer)
 56 |     layer=Dense(action_length,activation='relu')(layer)
 57 |     print(layer)
 58 |     output=layer
 59 |     model=Model(inputs=main_input,outputs=output)
 60 |     model.compile(optimizer='adam',loss='mse',metrics=['binary_accuracy'])
 61 |    
 62 |     # ------------------模型保存 (model saving)------------------
 63 |     tensorboard_path='Data/'+ USERNAME +'/Model/Action'
 64 |     # 训练可视化 (visualization)
 65 |     tbCallback=TensorBoard(log_dir=tensorboard_path,histogram_freq=0,write_graph=True,write_grads=True,write_images=True,embeddings_freq=0,embeddings_layer_names=None,embeddings_metadata=None)
 66 |     # 保存最好的模型 (save the best model)
 67 |     checkpoint=ModelCheckpoint(save_path,monitor='loss',verbose=0,save_best_only='True')
 68 |     # -----------------模型训练 (model training)--------------------
 69 |     model.fit(x_train,y_train,batch_size=6,epochs=25,shuffle=True,callbacks=[tbCallback,checkpoint])
 70 |     # -----------------数据预测 (data prediction)---------------------
 71 |     x_test=np.loadtxt(files_test,delimiter=',')
 72 |     y_pred=model.predict(x_test)
 73 |     np.savetxt(predict_save,y_pred,fmt='%f',delimiter=',')
 74 | 
 75 |     
 76 |     # # --------------------------- end ------------
 77 |     # pauses=input("save this file? Y/N : \n ")
 78 |     # if pauses=='Y'or pauses=='y':
 79 |     # #     model.save(save_path)
 80 |     # #     print("This Model have saved! ")
 81 |     #     np.savetxt(predict_save,y_pred,fmt='%f',delimiter=',')
 82 |     #     print("Predict files Model have saved! ")
 83 |     # else:
 84 |     #     print("Stop!")
 85 |     #     exit(0)
 86 | 
 87 | 
 88 | # ----------------------------- test ----------------
 89 | def test(files_test,save_path,predict_save):
 90 |     # labels_name='Data/'+username+'/feature/label_test.csv'
 91 |     x_test=np.loadtxt(files_test,delimiter=',')
 92 |     model=load_model(save_path)
 93 |     pred=model.predict(x_test)
 94 |     print(np.shape(pred))
 95 |     # pred 矩阵中如果大于 0.5 保持不变，否则置 0）
 96 |     pred=np.where(pred>0,pred,0)
 97 |     np.savetxt(predict_save,pred,fmt='%f',delimiter=',')
 98 | 
 99 | #  ------- define my loss 
100 | def my_loss_forFeatures(label_test,predict_save,myloss_save,figure_save):
101 |     y_true=np.loadtxt(label_test,delimiter=',')
102 |     y_pred=np.loadtxt(predict_save,delimiter=',')
103 |     batch_size=np.shape(y_pred)[0]
104 |     paras=np.array([1.2,1,0.2,0,0,0,0,1,0.1,1.2,\
105 |                         2,1,1.5,\
106 |                         0.5,1,1,0.5,0.1,0.7,\
107 |                         0.5,1,1,0.5,0.1,0.7,\
108 |                         0.5,1,1,0.5,0.1,0.7,\
109 |                         0.5,1,1,0.5,0.1,0.7])
110 |     All_loss=[]
111 |     for i in range(batch_size):
112 |         # Times=np.square(((y_pred[i]+0.6).astype(np.int32)-y_true[i]))
113 |         # ---- Original 
114 |         Times=np.square(np.multiply((y_pred[i]-y_true[i]),paras))
115 |         sums=Times.sum()/37
116 |         All_loss.append(sums)
117 |     x_list=range(0,batch_size)    
118 |     np.savetxt(myloss_save,All_loss,fmt='%f',delimiter=',')
119 |     # --------- draw pictures 
120 |     plt.figure()
121 |     plt.plot(x_list,All_loss)
122 |     # plt.show()
123 |     plt.savefig(figure_save)
124 |         # print(np.shape(loss))
125 | 
126 | def Calculate_deviations(files_test,label_test,save_path,loss_save,figure_save,action_length):
127 |     x_test=np.loadtxt(files_test,delimiter=',')
128 |     y_test=np.loadtxt(label_test,delimiter=',')
129 |     # print (np.shape(x_test))
130 |     # print(np.shape([x_test[0]]))
131 |     # print(len(x_test))
132 |     # exit (0)
133 |     model=load_model(save_path)
134 |     # exit(0)
135 |     All_loss=[]
136 |     x_list=range(0,len(x_test))
137 |     for i in range (len(x_test)):
138 |         x_small_test=np.reshape(x_test[i],(1,4*action_length))
139 |         y_small_test=np.reshape(y_test[i],(1,action_length))
140 |         loss,acc=model.evaluate(x=x_small_test,y=y_small_test,verbose=0)
141 |         All_loss.append(loss)
142 |     np.savetxt(loss_save,All_loss,fmt='%f',delimiter=',')
143 |     # --------- draw pictures 
144 |     plt.figure()
145 |     plt.plot(x_list,All_loss)
146 |     plt.xlabel('Days',fontsize=14)
147 |     plt.ylabel('WDD loss',fontsize=14)
148 |     # plt.show()
149 |     plt.savefig(figure_save)
150 |     # exit(0)
151 | 
152 | # ---------------------------------- for all  users --------------------------------
153 | if __name__ == "__main__":
154 | 
155 | 
156 |     # user_sets={'EDB0714':29,'TNM0961':32,'HXL0968':33}
157 | 
158 |     # -------- run model for every user separately or it will report errors because of the cache ----------- 
159 |     user_sets={'EDB0714':29}
160 |     # user_sets={'TNM0961':32}
161 |     # user_sets={'HXL0968':33}
162 |     
163 |     # ----------save the best model
164 |     for username,length in user_sets.items():
165 |         USERNAME=username
166 |         action_length=length
167 | 
168 |         folder='Data/'+ USERNAME +'/Model/Action/'
169 |         save_path=folder+'model.h5'
170 |         files_train='Data/'+ USERNAME+'/sequence/'+'data_train.csv'
171 |         labels_train='Data/'+USERNAME+'/sequence/'+'label_train.csv'
172 |         files_test='Data/'+USERNAME+'/sequence/'+'data_test.csv'
173 |         label_test='Data/'+USERNAME+'/sequence/'+'label_test.csv'
174 |         predict_save='Data/'+USERNAME+'/sequence/'+'predict.csv'
175 |         loss_save='Data/'+USERNAME+'/sequence/'+'loss.csv'
176 |         figure_save='Data/'+USERNAME+'/sequence/'+'loss.jpg'
177 |         path_check(folder)
178 | 
179 |         # ------------------------- 运行模型 (trining model)-------------------------
180 |         train(files_train,labels_train,save_path,files_test,predict_save,action_length)
181 |         
182 | 
183 | # ---------------- calculate all deviations for all data(train+test)
184 |         predict_save='Data/'+USERNAME+'/sequence/'+'predict_all.csv'
185 |         files_all='Data/'+USERNAME+'/sequence/'+'data_all.csv'
186 |         labels_all='Data/'+USERNAME+'/sequence/'+'label_all.csv'
187 |         loss_save='Data/'+USERNAME+'/sequence/'+'loss_all.csv'
188 |         figure_save='Data/'+USERNAME+'/sequence/'+'loss_all.jpg'
189 |         
190 |         Calculate_deviations(files_all,labels_all,save_path,loss_save,figure_save,action_length)
191 | 
192 | # -----------------------------------------
193 | 
194 | # -----------------------------------------------------------------
195 | 


--------------------------------------------------------------------------------
/4-Static_Feature_Training.py:
--------------------------------------------------------------------------------
  1 | from keras.models import Sequential,load_model,Model
  2 | from keras.layers import Dense, Activation,Embedding,Conv2D,MaxPooling2D,Reshape,BatchNormalization,Dropout,Input,concatenate,GlobalAveragePooling2D,Flatten,ConvLSTM2D,ConvLSTM2DCell,LSTM,Conv3D
  3 | from keras.optimizers import adam
  4 | import numpy as np 
  5 | import linecache
  6 | import tensorflow as tf 
  7 | import os
  8 | from keras import losses,metrics
  9 | from keras.callbacks import TensorBoard, ModelCheckpoint
 10 | import matplotlib.pyplot as plt
 11 | 
 12 | 
 13 | 
 14 | def path_check(path):
 15 |     folders=os.path.exists(path)
 16 |     if not folders:
 17 |         print("Create new folders: ' %s ' successfully!"%(path))
 18 |         os.makedirs(path)
 19 |     else:
 20 |         print("The folder  ' %s ' exits, Please check!"%(path))
 21 |         os._exit(0)
 22 | 
 23 | def count_line(files_in):
 24 |     file_open=open(files_in,'r')
 25 |     count=0
 26 |     for line in file_open:
 27 |         count+=1
 28 |     return count   
 29 | 
 30 | def data_generator(files_name,num_days=5,batch_size=30):
 31 |     # 删除最后一行数据，因为可能会有缺漏
 32 |     counts=count_line(files_name)-1
 33 |     real_counts=counts-num_days+1
 34 |     rounds=int(real_counts/batch_size)
 35 |     # print (real_counts)
 36 |     while (1):   
 37 |         for i in range(rounds):
 38 |             data_batch=[]
 39 |             label_batch=[]
 40 |             for j in range (batch_size):
 41 |                 data_index=i+j+1
 42 |                 label_index=data_index+4
 43 |                 data_one=linecache.getline(files_name,data_index).strip().split(',')+linecache.getline(files_name,data_index+1).strip().split(',')+linecache.getline(files_name,data_index+2).strip().split(',')+linecache.getline(files_name,data_index+3).strip().split(',')
 44 |                 labe_data=linecache.getline(files_name,label_index).strip().split(',')
 45 |                 data_batch.append(data_one)
 46 |                 label_batch.append(labe_data)
 47 |             # print(np.shape(labe_data))
 48 |             # print (data_one)
 49 |             # exit (0)
 50 |             data_batch=np.array(data_batch)
 51 |             label_batch=np.array(label_batch)
 52 |             data_out=np.reshape(data_batch,(batch_size,192))
 53 |             label_out=np.reshape(label_batch,(batch_size,48))
 54 |             i=i+batch_size+1
 55 |             yield data_out,label_out
 56 | 
 57 | 
 58 | 
 59 | def train(files_train,labels_train,save_path,files_test,predict_save):
 60 |     """ Deep Learning model for users' features
 61 |     # Params:
 62 |     ----
 63 |         files_train: training data (data for model training)
 64 |         labels_train: labels of training data
 65 |         save_path: the path of model that is going to be saved 
 66 |         files_test: test data 
 67 |         predict_save: the predicted labels of test data 
 68 |         
 69 |     """
 70 |     # ---------------模型输入 (input)---------------
 71 |     x_train=np.loadtxt(files_train,delimiter=',')
 72 |     y_train=np.loadtxt(labels_train,delimiter=',')
 73 | 
 74 |     # ---------------模型结构 (model structure)---------------
 75 |     main_input=Input(shape=(148,),dtype='float32',name='MainInput')
 76 |     Inputs=Reshape((4,37))(main_input)
 77 |     layer=LSTM(100,return_sequences=True,activation='tanh')(Inputs)
 78 |     layer=LSTM(160,return_sequences=False,activation='tanh')(layer)
 79 |     layer=Dense(37,activation='relu')(layer)
 80 |     output=layer
 81 |     model=Model(inputs=main_input,outputs=output)
 82 |     model.compile(optimizer='adam',loss='mse',metrics=['binary_accuracy'])
 83 | 
 84 |     # ------------------模型保存 (save the model)------------------
 85 |     tensorboard_path='Data/'+ USERNAME +'/Model/Feature'
 86 |     tbCallback=TensorBoard(log_dir=tensorboard_path,histogram_freq=0,write_graph=True,write_grads=True,write_images=True,embeddings_freq=0,embeddings_layer_names=None,embeddings_metadata=None)
 87 |     checkpoint=ModelCheckpoint(save_path,monitor='loss',verbose=0,save_best_only='True')
 88 |     model.fit(x_train,y_train,batch_size=6,epochs=30,shuffle=True,callbacks=[tbCallback,checkpoint])
 89 |     
 90 |     # -----------------数据预测 (data prediction )---------------------
 91 |     x_test=np.loadtxt(files_test,delimiter=',')
 92 |     y_pred=model.predict(x_test)
 93 |     np.savetxt(predict_save,y_pred,fmt='%f',delimiter=',')
 94 | 
 95 | 
 96 | def retrain(files_train,labels_train,save_path,files_test,predict_save):
 97 |     """
 98 |     model retraining 
 99 |     """
100 |     x_train=np.loadtxt(files_train,delimiter=',')
101 |     y_train=np.loadtxt(labels_train,delimiter=',')
102 |     model=load_model(save_path)
103 | 
104 |     # ----------------- load the model-----
105 |     tensorboard_path='Data/'+ USERNAME +'/Model/Feature'
106 |     tbCallback=TensorBoard(log_dir=tensorboard_path,histogram_freq=0,write_graph=True,write_grads=True,write_images=True,embeddings_freq=0,embeddings_layer_names=None,embeddings_metadata=None)
107 |     checkpoint=ModelCheckpoint(save_path,monitor='loss',verbose=0,save_best_only='True')
108 | 
109 |     model.fit(x_train,y_train,batch_size=6,epochs=800,shuffle=True,callbacks=[tbCallback,checkpoint])
110 |     
111 |     # ---------------- add test -------
112 |     x_test=np.loadtxt(files_test,delimiter=',')
113 |     y_pred=model.predict(x_test)
114 |     np.savetxt(predict_save,y_pred,fmt='%f',delimiter=',')
115 | 
116 | # ----------------------------- test ----------------
117 | def test(files_test,save_path,predict_save):
118 |     
119 |     # labels_name='Data/'+username+'/feature/label_test.csv'
120 |     x_test=np.loadtxt(files_test,delimiter=',')
121 |     model=load_model(save_path)
122 |     pred=model.predict(x_test)
123 |     print(np.shape(pred))
124 |     # pred 矩阵中如果大于 0.5 保持不变，否则置 0）
125 |     pred=np.where(pred>0.47,pred,0)
126 |     np.savetxt(predict_save,pred,fmt='%f',delimiter=',')
127 |     return pred
128 | 
129 | 
130 | #  define my loss
131 | def my_loss_forFeatures(label_test,predict_save,myloss_save,figure_save):
132 |     y_true=np.loadtxt(label_test,delimiter=',')
133 |     y_pred=np.loadtxt(predict_save,delimiter=',')
134 |     batch_size=np.shape(y_pred)[0]
135 |     paras=np.array([1.2,1,0.2,0,0,0,0,1,0.1,1.2,\
136 |                         2,1,1.5,\
137 |                         0.5,1,1,0.5,0.1,0.7,\
138 |                         0.5,1,1,0.5,0.1,0.7,\
139 |                         0.5,1,1,0.5,0.1,0.7,\
140 |                         0.5,1,1,0.5,0.1,0.7])
141 |     All_loss=[]
142 |     for i in range(batch_size):
143 |         # times=np.square(((y_pred[i]+0.6).astype(np.int32)-y_true[i]))
144 |         # ---- Original 
145 |         times=np.square(np.multiply((y_pred[i]-y_true[i]),paras))
146 |         sums=times.sum()/37
147 |         All_loss.append(sums)
148 |     x_list=range(0,batch_size)    
149 |     np.savetxt(myloss_save,All_loss,fmt='%f',delimiter=',')
150 |     # --------- draw pictures 
151 |     plt.figure()
152 |     plt.plot(x_list,All_loss)
153 |     plt.xlabel('Days',fontsize=14)
154 |     plt.ylabel('WDD loss',fontsize=14)
155 |     # plt.show()
156 |     plt.savefig(figure_save)
157 |         # print(np.shape(loss))
158 | 
159 | def Calculate_deviations(files_test,label_test,save_path,loss_save,figure_save):
160 |     x_test=np.loadtxt(files_test,delimiter=',')
161 |     y_test=np.loadtxt(label_test,delimiter=',')
162 |     # print (np.shape(x_test))
163 |     # print(np.shape([x_test[0]]))
164 |     # print(len(x_test))
165 |     # exit (0)
166 |     model=load_model(save_path)
167 |     # exit(0)
168 |     All_loss=[]
169 |     x_list=range(0,len(x_test))
170 |     for i in range (len(x_test)):
171 |         x_small_test=np.reshape(x_test[i],(1,148))
172 |         y_small_test=np.reshape(y_test[i],(1,37))
173 |         loss,acc=model.evaluate(x=x_small_test,y=y_small_test,verbose=0)
174 |         All_loss.append(loss)
175 |     np.savetxt(loss_save,All_loss,fmt='%f',delimiter=',')
176 |     # --------- draw pictures 
177 |     plt.figure()
178 |     plt.plot(x_list,All_loss)
179 |     plt.xlabel('Days',fontsize=14)
180 |     plt.ylabel('MSE loss',fontsize=14)
181 |     # plt.show()
182 |     plt.savefig(figure_save)
183 |     # exit(0)
184 | 
185 | 
186 | 
187 | 
188 | 
189 | if __name__ == "__main__":
190 |     
191 |     # user_sets={'EDB0714':29,'TNM0961':32,'HXL0968':33}
192 | 
193 |     # -------- run model for every user separately or it will report errors because of the cache ----------- 
194 |     user_sets={'EDB0714':29}
195 |     # user_sets={'TNM0961':32}
196 |     # user_sets={'HXL0968':33}
197 | 
198 |     for username,length in user_sets.items():
199 |         USERNAME=username 
200 |         folder='Data/'+ USERNAME +'/Model/Feature/'
201 |         save_path=folder+'model.h5'
202 |         files_train='Data/'+USERNAME+'/feature/'+'data_train.csv'
203 |         labels_train='Data/'+USERNAME+'/feature/'+'label_train.csv'
204 |         files_test='Data/'+USERNAME+'/feature/'+'data_test.csv'
205 |         predict_save='Data/'+USERNAME+'/feature/'+'predict.csv'
206 |         label_test='Data/'+USERNAME+'/feature/'+'label_test.csv'
207 |         loss_save='Data/'+USERNAME+'/feature/'+'loss.csv'
208 |         figure_save='Data/'+USERNAME+'/feature/'+'loss.jpg'
209 |         figure_my_save='Data/'+USERNAME+'/feature/'+'myloss.jpg'
210 |         myloss_save='Data/'+USERNAME+'/feature/'+'myloss.csv'
211 | 
212 |         train(files_train,labels_train,save_path,files_test,predict_save)
213 |         # retrain(files_train,labels_train,save_path,files_test,predict_save)
214 | 
215 | # ------------ calculate all deviations for all data(train+test)
216 |         predict_save='Data/'+USERNAME+'/feature/'+'predict_all.csv'
217 |         files_all='Data/'+USERNAME+'/feature/'+'data_all.csv'
218 |         labels_all='Data/'+USERNAME+'/feature/'+'label_all.csv'
219 |         loss_save='Data/'+USERNAME+'/feature/'+'loss_all.csv'
220 |         figure_save='Data/'+USERNAME+'/feature/'+'loss_all.jpg'
221 |         figure_my_save='Data/'+USERNAME+'/feature/'+'myloss_all.jpg'
222 |         myloss_save='Data/'+USERNAME+'/feature/'+'myloss_all.csv'
223 |         test(files_all,save_path,predict_save)
224 |         Calculate_deviations(files_all,labels_all,save_path,loss_save,figure_save)
225 |         my_loss_forFeatures(labels_all,predict_save,myloss_save,figure_my_save)
226 | 
227 | # ----------------------------------------------------------       
228 | 
229 | 


--------------------------------------------------------------------------------
/5-Role-Feature_and_Mix.py:
--------------------------------------------------------------------------------
  1 | import numpy as np 
  2 | import linecache
  3 | import matplotlib.pyplot as plt
  4 | import os
  5 | # from FeatureExtract import path_check
  6 | 
  7 | 
  8 | def path_check(path):
  9 |     '''
 10 |     Check the input path
 11 |     '''
 12 |     folders=os.path.exists(path)
 13 |     if not folders:
 14 |         print("Create new folders: ' %s ' successfully!"%(path))
 15 |         os.makedirs(path)
 16 |     else:
 17 |         print("The folder  ' %s ' exits, Please check!"%(path))
 18 |         pass
 19 |         # os._exit(0)
 20 | 
 21 | def role_feature():
 22 |     file_name='/feature/data_out.csv'    
 23 |     user_sets=['ASD0577', 'AAL0706', 'ASM0575', 'GCG0951', 'AAV0450', 'TDG0962', 'CCM0136', 'FRR0832', 'KPP0452', 'ABM0845', 'MJB0588', 'WJD0576', 'ILH0958', 'FEB0306', 'JJB0700', 'AHD0848', 'LDM0587', 'MLG0475', 'NCK0295', 'YJV0699',]
 24 |     All_users_features=np.zeros((37))
 25 |     for username in user_sets:
 26 |         path=username+file_name
 27 |         All_features=np.loadtxt(path,delimiter=',')
 28 |         Nums=len(All_features)
 29 |         Aver_features=np.zeros((37))
 30 |         # print(np.shape(Aver_features))
 31 |         for matrix in All_features:
 32 |             Aver_features=Aver_features+matrix
 33 |         Aver_features=Aver_features/Nums
 34 |         All_users_features=All_users_features+Aver_features
 35 |             # print (np.shape(matrix))
 36 |     role_features=All_users_features/len(user_sets)
 37 |     role_features=np.reshape(role_features,(1,37))
 38 |     np.savetxt('Role_feature.csv',role_features,delimiter=',',fmt='%f')
 39 | 
 40 | def deviations_for_users(username):
 41 |     '''
 42 |     Calculate the  deviations between user's daily feature and role feature.
 43 |     '''
 44 | 
 45 |     # username='EDB0714'
 46 |     file_name='/feature/label_all.csv'
 47 |     role_feature_file='Data/'+ 'Role_feature.csv'
 48 |     Role_features=np.loadtxt(role_feature_file,delimiter=',')
 49 |     user_features=np.loadtxt('Data/'+ username+file_name,delimiter=',')
 50 |     All_loss=[]
 51 |     for matrix in user_features:
 52 |         paras=np.array([1.2,1,0.2,0,0,0,0,1,0.1,1.2,\
 53 |                         2,1,1.5,\
 54 |                         0.5,1,1,0.5,0.1,0.7,\
 55 |                         0.5,1,1,0.5,0.1,0.7,\
 56 |                         0.5,1,1,0.5,0.1,0.7,\
 57 |                         0.5,1,1,0.5,0.1,0.7])
 58 |         deviations=np.square(np.multiply((Role_features-matrix),paras))
 59 |         deviations=deviations.sum()/37
 60 |         All_loss.append(deviations)
 61 |     np.savetxt('Data/'+ username+'/Model/Role_loss.csv',All_loss,delimiter=',',fmt='%f')
 62 |     x_list=range(0,len(user_features))
 63 |     print(np.shape(All_loss))
 64 |     plt.figure()
 65 |     plt.plot(x_list,All_loss)
 66 |     plt.xlabel('Days',fontsize=14)
 67 |     plt.ylabel('WDD loss',fontsize=14)
 68 |     # plt.show()
 69 |     plt.savefig('Data/'+ username+'/Model/Role_features.jpg')
 70 | 
 71 | def count_line(files_in):
 72 |     file_open=open(files_in,'r')
 73 |     count=0
 74 |     for line in file_open:
 75 |         count+=1
 76 |     file_open.close()
 77 |     return count   
 78 | 
 79 | 
 80 | 
 81 | 
 82 | # use the code below step by step.
 83 | if __name__ == "__main__":
 84 | 
 85 |     # ------ Calculate the role feature ---------
 86 |     # role_feature() 
 87 |     user_sets=['EDB0714','TNM0961','HXL0968']
 88 |     # ----- step 1  每个用户单独计算 role features 
 89 |     for user in user_sets:
 90 |         deviations_for_users(user)
 91 |     # ------------------------------------
 92 |     # ------ step 2 每个用户的各种偏差度进行拼接 (mix data)------
 93 |     for username in user_sets:
 94 |         user_path='Data/'+ username+'/Mix'
 95 |         path_check(user_path)
 96 |         file_feature='/feature/myloss_all.csv'
 97 |         file_sequence='/sequence/loss_all.csv'
 98 |         file_role='/Model/Role_loss.csv'
 99 |         file_save=user_path+'/Mix_loss.csv'
100 |         # 加载各个特征的loss
101 |         feature_loss=np.loadtxt('Data/'+ username+file_feature,delimiter=',')
102 |         feature_loss=np.reshape(feature_loss,(len(feature_loss),1))
103 |         sequence_loss=np.loadtxt('Data/'+ username+file_sequence,delimiter=',')
104 |         sequence_loss=np.reshape(sequence_loss,(len(sequence_loss),1))
105 |         role_loss=np.loadtxt('Data/'+ username+file_role,delimiter=',')
106 |         role_loss=np.reshape(role_loss,(len(role_loss),1))
107 |         mix_loss=np.concatenate((feature_loss,sequence_loss,role_loss),axis=1)
108 |         np.savetxt(file_save,mix_loss,delimiter=',',fmt='%f')
109 |         print(np.shape(role_loss))
110 |         print(np.shape(mix_loss))
111 |     # ----- step 3 拼接的数据进行标签 (然后根据CERT数据集中的恶意行为进行标签的修正) Generate the label according to the source data
112 |     # -------  the number behind the user is the firt day that the users' anomalous behavior is emerging accoriding to the source data in CERT dataset ------------
113 |     user_sets={'EDB0714':198,'TNM0961':198,'HXL0968':165} 
114 |     for username,nums in user_sets.items():
115 |             user_path='Data/'+ username+'/Mix'
116 |             file_save=user_path+'/label_loss.csv'
117 |             file_read=user_path+'/Mix_loss.csv'
118 |             Num_lines=count_line(file_read)
119 |             fileopen=open(file_save,'wt')
120 |             index=0
121 |             while index<Num_lines:
122 |                 if index<nums-1:
123 |                     fileopen.writelines('0,1\n')
124 |                 else:
125 |                     fileopen.writelines('1,0\n')
126 |                 index=index+1
127 |             fileopen.close()
128 |     # -------- step 4 进行各个用户数据的混合：(mix all data) --------
129 |     mix_path='Data/Mix'
130 |     path_check(mix_path)
131 |     file_save=open(mix_path+'/Mix_all_loss.csv','wt')
132 |     label_save=open(mix_path+'/Mix_all_label.csv','wt')
133 |     for username in user_sets:
134 |         user_path='Data/'+ username+'/Mix'
135 |         mix_loss=user_path+'/Mix_loss.csv'
136 |         mix_label=user_path+'/label_loss.csv'
137 |         loss_open=open(mix_loss,'r')
138 |         label_open=open(mix_label,'r')
139 |         for line in loss_open:
140 |             file_save.writelines(line)
141 |         loss_open.close()
142 |         for line in label_open:
143 |             label_save.writelines(line)
144 |         label_open.close()
145 | 
146 | 
147 | 
148 | 
149 | 
150 | 
151 |     # exit(0)
152 | 
153 | 
154 | 
155 | 


--------------------------------------------------------------------------------
/6-Mix_data_for_MLP.py:
--------------------------------------------------------------------------------
 1 | import linecache,random
 2 | 
 3 | 
 4 | def count_line(files_in):
 5 |     file_open=open(files_in,'r')
 6 |     count=0
 7 |     for line in file_open:
 8 |         count+=1
 9 |     file_open.close()
10 |     return count   
11 | 
12 | def Random_Num(index,number):
13 |     '''
14 |     Generate a list of index from index randomly
15 |      index : the index of files
16 |      number : the length of list generated
17 |      return : a list of index  
18 |     '''
19 |     return random.sample(list(index),number)
20 | 
21 | def train_test_generate(data_all,label_all,train_data_save,train_label_save,test_data_save,test_label_save,rate=0.8):
22 |     '''
23 |     we extract 'rate' (the defaults is 0.8) percent data for training, and the rest for testing. 
24 |     
25 |     '''
26 |     
27 |     data_in=open(data_all,'r')
28 |     label_in=open(label_all,'r')
29 |     data_train=open(train_data_save,'wt')
30 |     data_test=open(test_data_save,'wt')
31 |     label_train=open(train_label_save,'wt')
32 |     label_test=open(test_label_save,'wt')
33 |     data_num=count_line(data_all)
34 |     train_num=data_num*rate
35 |     # print(train_num)
36 |     index=0
37 |     for line in data_in:
38 |         if index<train_num:
39 |             data_train.writelines(line)
40 |         else:
41 |             data_test.writelines(line)
42 |         index=index+1
43 | 
44 |     data_train.close()
45 |     data_test.close()
46 | 
47 |     index=0
48 |     for label in label_in:
49 |         if index<train_num:
50 |             label_train.writelines(label)
51 |         else:
52 |             label_test.writelines(label)
53 |         index=index+1
54 |     label_train.close()
55 |     label_test.close()
56 |     data_in.close()
57 |     label_in.close()
58 | 
59 | 
60 | if __name__ == "__main__":
61 |     working_folder='Data/Mix/'
62 |     # ----- disorder all mixed data ------
63 |     file_open='Data/Mix/'+'Mix_all_loss.csv'
64 |     label_open='Data/Mix/'+'Mix_all_label.csv'
65 |     file_save=open('Data/Mix/'+'Disorder_loss.csv','wt')
66 |     label_save=open('Data/Mix/'+'Disorder_label.csv','wt')
67 |     line_counts=count_line(label_open)
68 |     index=[n for n in range(0,line_counts)] 
69 |     Random_index=Random_Num(index,line_counts)
70 |     # print(Random_index)
71 |     for num in Random_index:
72 |         line=linecache.getline(file_open,num+1)
73 |         label=linecache.getline(label_open,num+1)
74 |         file_save.writelines(line)
75 |         label_save.writelines(label)
76 |     file_save.close()
77 |     label_save.close()
78 |     # --------- generate tran and test data for MLP
79 |     data_all='Data/Mix/'+'Disorder_loss.csv'
80 |     label_all='Data/Mix/'+'Disorder_label.csv'
81 |     data_train='Data/Mix/'+'train.csv'
82 |     label_train='Data/Mix/'+'label_train.csv'
83 |     data_test='Data/Mix/'+'test.csv'
84 |     label_test='Data/Mix/'+'label_test.csv'
85 |     train_test_generate(data_all,label_all,data_train,label_train,data_test,label_test,rate=0.7)
86 | 
87 | 
88 | 
89 | 
90 | 
91 | 
92 | 
93 | 
94 | 
95 | 
96 | 
97 | 
98 | 


--------------------------------------------------------------------------------
/7-MLP_Training_and_Metrics.py:
--------------------------------------------------------------------------------
  1 | from keras.models import Sequential,load_model,Model
  2 | from keras.layers import Dense, Activation,Embedding,Conv2D,MaxPooling2D,Reshape,BatchNormalization,Dropout,Input,concatenate,GlobalAveragePooling2D,Flatten,ConvLSTM2D,ConvLSTM2DCell,LSTM,Conv3D
  3 | from keras.optimizers import adam
  4 | import numpy as np 
  5 | import linecache
  6 | from keras import losses,metrics
  7 | from keras.callbacks import TensorBoard, ModelCheckpoint
  8 | import matplotlib.pyplot as plt
  9 | import csv
 10 | from sklearn.metrics import roc_curve,auc
 11 | from mpl_toolkits.mplot3d import Axes3D
 12 | 
 13 | 
 14 | # losses.binary_crossentropy
 15 | 
 16 | def count_line(files_in):
 17 |     file_open=open(files_in,'r')
 18 |     count=0
 19 |     for line in file_open:
 20 |         count+=1
 21 |     file_open.close()
 22 |     return count   
 23 | 
 24 | def train(files_train,labels_train,model_path,files_test,label_test,predict_save):
 25 |    
 26 |     x_train=np.loadtxt(files_train,delimiter=',')
 27 |     y_train=np.loadtxt(labels_train,delimiter=',')
 28 |     # y_train=np.reshape(y_train,(-1,40,1))
 29 | 
 30 |     # --------- model structure -----------
 31 |     main_input=Input(shape=(3,),dtype='float32',name='MainInput')
 32 |     layer=Dense(10)(main_input)
 33 |     # layer=BatchNormalization()(layer)
 34 | 
 35 |     # layer=Dropout(0.5)(layer)
 36 |     layer=Dense(2,activation='softmax')(layer)
 37 |     # layer=LSTM(96,return_sequences=False,activation='tanh')(layer)
 38 |     
 39 |     output=layer
 40 | 
 41 |     # ------------------------
 42 |     model=Model(inputs=main_input,outputs=output)
 43 |     model.compile(optimizer='adam',loss='binary_crossentropy',metrics=['binary_accuracy'])
 44 | 
 45 |     tensorboard_path='./'
 46 |     tbCallback=TensorBoard(log_dir=tensorboard_path,histogram_freq=0,write_graph=True,write_grads=True,write_images=True,embeddings_freq=0,embeddings_layer_names=None,embeddings_metadata=None)
 47 |     checkpoint=ModelCheckpoint(model_path,monitor='loss',verbose=0,save_best_only='True')
 48 | 
 49 |     model.fit(x_train,y_train,batch_size=6,epochs=80,shuffle=True,callbacks=[tbCallback,checkpoint])
 50 |     
 51 |     # model.fit(x_train,y_train,batch_size=12,epochs=700,shuffle=True)
 52 |     # ---------------- add test -------
 53 |     x_test=np.loadtxt(files_test,delimiter=',')
 54 |     y_test=np.loadtxt(label_test,delimiter=',')
 55 |     y_pred=model.predict(x_test)
 56 |     np.savetxt(predict_save,y_pred,fmt='%f',delimiter=',')
 57 |     loss,acc=model.evaluate(x=x_test,y=y_test,verbose=0)
 58 |     print (acc)
 59 |     # -------------------------
 60 |     # model.save(save_path)
 61 |     # return y_train
 62 | 
 63 | def Calculatte(pred_file,label_file):
 64 |     # file_label=open(label_file,'r')
 65 |     Drn=0
 66 |     Dra=0
 67 |     with open(pred_file,'r') as csvfile:
 68 |         reader=csv.reader(csvfile)
 69 |         # i:0-n
 70 |         for i,rows in enumerate (reader):
 71 |             # print (i) #row is a list
 72 |             Pred=int(rows[0])
 73 |             line=linecache.getline(label_file,i+1)
 74 |             Label=int(line[0])
 75 |             if (Label==0 and Pred==0):
 76 |                 Drn+=1
 77 |             if (Label==1 and Pred==1):
 78 |                 Dra+=1
 79 |     print('Drn: ',Drn,' Dra: ',Dra)
 80 |     return Dra+Drn
 81 | 
 82 | 
 83 | def Count_nor_ano(label):
 84 |     Num_nor=0
 85 |     Num_ano=0
 86 |     with open(label,'r') as csvfile:
 87 |         reader=csv.reader(csvfile)
 88 |         # i:0-n
 89 |         for i,rows in enumerate (reader):
 90 |             if int(rows[0])==0:
 91 |                 Num_nor+=1
 92 |             if int(rows[0])==1:
 93 |                 Num_ano+=1
 94 |     print ('Num_normal: ',Num_nor,' Num_anomalous: ',Num_ano)
 95 |     return Num_ano+Num_nor
 96 | 
 97 | def figure_ponit():
 98 |     # normal
 99 |     x1=[]
100 |     y1=[]
101 |     z1=[]
102 |     # anomalous
103 |     x2=[]
104 |     y2=[]
105 |     z2=[]
106 |     all_data_in='Data/Mix/'+'Mix_all_loss.csv'
107 |     all_label_in='Data/Mix/'+'Mix_all_label.csv'
108 |     line_counts=count_line(all_label_in)
109 |     for i in range (line_counts):
110 |         line=linecache.getline(all_data_in,i+1)
111 |         label=linecache.getline(all_label_in,i+1)
112 |         line=line.strip()
113 |         line=line.split(',')
114 |         label=label.strip()
115 |         label=label.split(',')
116 |         # print(line)
117 |         # print(label[0])
118 |         # exit(0)
119 |         if label[0]=='0':
120 |             x1.append(float(line[0]))
121 |             y1.append(float(line[1]))
122 |             z1.append(float(line[2]))
123 |             # print (x1,y1,z1)
124 |             # exit(0)
125 |         else:
126 |             x2.append(float(line[0]))
127 |             y2.append(float(line[1]))
128 |             z2.append(float(line[2]))
129 | 
130 |     
131 |     fig=plt.figure()
132 |     ax3d=Axes3D(fig)
133 |     ax3d.scatter(x1,y1,z1,c='g',label='Normal')
134 |     ax3d.scatter(x2,y2,z2,c='r',label='Anomalous')
135 | 
136 |     ax3d.set_zlabel('WDD of Role features', fontdict={'size': 13, 'color': 'black'})
137 |     ax3d.set_ylabel('WDD of Action sequence', fontdict={'size': 13, 'color': 'black'})
138 |     ax3d.set_xlabel('WDD of Action Features', fontdict={'size': 13, 'color': 'black'})
139 | 
140 |     ax3d.legend()
141 |     plt.show()
142 |     # plt.savefig('Point.jpg')
143 | 
144 | 
145 | 
146 | 
147 | if __name__ == "__main__":
148 | 
149 |     working_folder='Data/Mix/'
150 |     files_train=working_folder+'train.csv'
151 |     labels_train=working_folder+'label_train.csv'
152 |     model_path=working_folder+'MLP.h5'
153 |     files_test=working_folder+'test.csv'
154 |     label_test=working_folder+'label_test.csv'
155 |     predict_save=working_folder+'predict.csv'
156 |     predict_label=working_folder+'predict_label.csv'
157 |     # ------ 训练MLP
158 |     train(files_train,labels_train,model_path,files_test,label_test,predict_save)
159 |     # -------------------------
160 | # ---------------对预测数据进行标签化 (add label for data)---------------
161 |     predicts=np.loadtxt(predict_save,delimiter=',')
162 |     pred=np.where(predicts>0.5,1,0)
163 |     np.savetxt(predict_label,pred,delimiter=',',fmt='%d')
164 |     # -------------------- end -----------------
165 |     # -------------- 对结果进行统计 ( metrics)：DRA ACC ...---------
166 |     all_label=Count_nor_ano(label_test)
167 |     pred=Calculatte(predict_label,label_test)
168 |     print (pred/all_label)
169 |     # -------------------- end --------------------
170 |     # -------- ROC curve --------------------
171 |     file_open=open(label_test,'r')
172 |     file_open_two=open(predict_save,'r')
173 |     y_true=[]
174 |     y_score=[]
175 |     for line in file_open:
176 |         if line[0]=='0':
177 |             y_true.append(1)
178 |         else:
179 |             y_true.append(0)
180 |     file_open.close()
181 | 
182 |     for line in file_open_two:
183 |         line=line.strip()
184 |         line=line.split(',')
185 |         y_score.append(float(line[1]))
186 |     file_open_two.close()
187 |     # print (y_score)
188 |     
189 |     fpr,tpr,threshold=roc_curve(y_true,y_score)
190 |     Auc=auc(fpr,tpr)
191 |     print('FPR: ',fpr)
192 |     print('TPR: ',tpr)
193 |     # --------------base line 
194 |     baseline_fpr=[0,0.03703704,0.03803704,0.04703704,0.06407407,0.07407407,0.07407407,0.11111111,0.11311111,0.14814815,0.15814815,0.35564815,0.46444444,0.46444444,1]
195 |     baseline_tpr=[0,0.16184971,0.26184971,0.26184971,0.63815029,0.83815029,0.83815029,0.88265896,0.88265896,0.88265896,0.98643931,0.99321965,0.99321965,1,1]
196 |     Baseline_Auc=auc(baseline_fpr,baseline_tpr)
197 | 
198 |     # print(Auc)
199 |     plt.figure()
200 |     plt.plot(fpr,tpr,color='red',label='MBS AUC = %0.3f)'% Auc)
201 |     plt.plot(baseline_fpr,baseline_tpr,color='blue',label='Baseline AUC = %0.3f)'% Baseline_Auc)
202 |     plt.plot([0, 1], [0, 1], color='navy',linestyle='--')
203 |     plt.xlim([0.0,1.0])
204 |     plt.ylim([0.0,1.05])
205 |     plt.xlabel('False Positive Rate',fontsize=14)
206 |     plt.ylabel('True Positive Rate',fontsize=14)
207 |     plt.title('Receiver operating characteristic',fontsize=14)
208 |     # 显示图示
209 |     plt.legend(fontsize=12)
210 |     # plt.show()
211 |     plt.savefig(working_folder+'ROC.jpg')
212 |     # -------------------------- end -----------------------------------
213 | # ------------- 三维散点图 (scatter diagram) ----------------
214 |     figure_ponit()
215 |     
216 | 
217 | 
218 |     
219 | 


--------------------------------------------------------------------------------
/Data/EDB0714/Mix/Mix_loss.csv:
--------------------------------------------------------------------------------
  1 | 0.189822,1.108167,1.225708
  2 | 1.033824,1.030719,1.160631
  3 | 0.680418,3.484405,2.128120
  4 | 0.651462,1.119858,1.281070
  5 | 1.417325,2.614433,0.340711
  6 | 0.392637,2.029116,1.260171
  7 | 0.765394,2.581327,0.903438
  8 | 0.476012,3.067587,1.557253
  9 | 0.704151,1.131555,0.633609
 10 | 0.555268,0.540050,1.342639
 11 | 0.548717,0.784870,0.681949
 12 | 1.263424,1.087532,1.822006
 13 | 1.184988,2.269872,2.082971
 14 | 1.004469,1.595726,0.483985
 15 | 0.246021,0.894869,0.776742
 16 | 0.331714,1.077577,1.367721
 17 | 0.509503,1.054945,1.200105
 18 | 0.583325,1.372019,1.368439
 19 | 0.372386,1.451576,1.210699
 20 | 0.246240,1.135703,1.100170
 21 | 0.267417,1.501771,0.853812
 22 | 0.772743,1.601499,0.383316
 23 | 0.174393,1.170918,1.091996
 24 | 0.495204,1.870671,0.853276
 25 | 0.875756,1.192595,0.727431
 26 | 0.324040,2.868531,1.468860
 27 | 0.721312,2.583979,0.659892
 28 | 1.292806,1.188223,1.656941
 29 | 0.530457,3.701290,1.958088
 30 | 0.837045,1.127407,0.947110
 31 | 0.687532,1.702074,0.348159
 32 | 0.932408,1.368357,1.036610
 33 | 0.891866,3.256800,1.989907
 34 | 0.259195,2.777088,1.373779
 35 | 0.389189,0.838125,1.316674
 36 | 0.516634,2.766960,1.376205
 37 | 0.451485,1.124365,0.734334
 38 | 0.339247,2.358960,1.743314
 39 | 0.362296,1.618802,0.575706
 40 | 0.562458,1.013119,0.666457
 41 | 1.087419,2.877237,1.096033
 42 | 0.881232,2.578161,1.904759
 43 | 0.628949,1.697103,0.435750
 44 | 0.592875,1.741222,0.422270
 45 | 0.468808,2.817449,1.844005
 46 | 1.074865,3.886403,2.025140
 47 | 0.425232,1.844222,0.557234
 48 | 0.510892,1.584316,0.927141
 49 | 0.810976,2.754529,0.636465
 50 | 0.409643,2.335002,1.125686
 51 | 0.327385,1.127169,1.186923
 52 | 0.527678,1.766707,0.496356
 53 | 0.351898,2.608537,1.111733
 54 | 0.259418,1.020952,1.180625
 55 | 0.363435,0.865381,1.213249
 56 | 0.506933,2.558506,1.068738
 57 | 0.259424,1.142744,0.789492
 58 | 0.766968,1.409397,0.335326
 59 | 1.177067,1.400189,1.142220
 60 | 0.664113,2.752491,1.362360
 61 | 0.703551,3.542991,1.919471
 62 | 0.515891,1.028948,0.874567
 63 | 0.390281,1.072075,0.844972
 64 | 0.644783,2.773572,1.646133
 65 | 1.026881,2.140502,1.779535
 66 | 0.332102,2.800769,1.289890
 67 | 0.616534,1.145713,0.896441
 68 | 0.729874,2.200090,1.708594
 69 | 0.304182,2.555966,1.420256
 70 | 0.393980,2.775197,1.387583
 71 | 0.553070,2.987806,1.452780
 72 | 0.395530,1.060931,1.131948
 73 | 0.449928,2.771090,2.118903
 74 | 0.613714,1.066467,1.377297
 75 | 0.419258,2.365166,1.533837
 76 | 0.172968,1.188662,0.671445
 77 | 0.740443,1.081814,0.772684
 78 | 0.274884,2.650863,1.126179
 79 | 0.466059,1.145044,1.221882
 80 | 0.188089,1.167317,0.871071
 81 | 0.534635,1.107834,1.033291
 82 | 0.536009,1.109586,0.858417
 83 | 0.384258,1.332119,0.579866
 84 | 0.314235,1.253710,1.097658
 85 | 0.496226,3.957128,1.989518
 86 | 1.065929,4.646476,3.231564
 87 | 0.880102,1.289643,0.622431
 88 | 0.117473,0.608852,0.980465
 89 | 0.458733,1.910743,0.421122
 90 | 1.110232,1.618356,0.670959
 91 | 0.513159,1.192824,0.689316
 92 | 0.656210,2.921651,1.350556
 93 | 0.507025,2.694421,1.063135
 94 | 0.892877,2.848868,1.542314
 95 | 0.312121,2.919386,1.233727
 96 | 1.172538,1.849680,0.502621
 97 | 0.376038,1.065543,1.154936
 98 | 0.291064,0.556249,1.181292
 99 | 0.747365,1.052178,1.016036
100 | 0.325116,2.749959,1.190414
101 | 0.686750,1.596364,0.864920
102 | 0.353108,0.645308,0.638332
103 | 0.428499,1.058564,0.789309
104 | 0.320541,1.043324,0.649536
105 | 0.930373,3.567299,1.916579
106 | 0.421343,1.584300,0.865389
107 | 0.719656,1.124230,0.653778
108 | 0.439714,2.809739,1.075187
109 | 0.934920,4.014019,1.973555
110 | 0.390370,2.616303,1.098222
111 | 0.259783,0.810357,0.735007
112 | 0.380566,0.994672,1.518037
113 | 0.422370,1.733539,0.679203
114 | 1.068246,1.751114,1.107212
115 | 0.563576,1.116261,1.024513
116 | 0.548636,2.870988,1.223780
117 | 0.437978,2.574785,1.738848
118 | 0.535257,2.603574,1.550029
119 | 0.497627,1.086767,0.931421
120 | 0.352346,2.368344,1.320398
121 | 0.322183,1.016116,1.178220
122 | 0.238204,2.564666,1.575789
123 | 0.530177,2.418218,1.210651
124 | 0.415785,0.835684,0.899092
125 | 0.463426,1.746314,0.528560
126 | 0.404340,1.017413,1.181191
127 | 0.410932,2.094999,0.261044
128 | 0.472595,1.432744,0.643093
129 | 0.143867,0.573603,0.955987
130 | 0.905937,1.654372,0.971739
131 | 0.405567,1.039572,1.274075
132 | 0.403380,0.579642,1.285781
133 | 0.373661,1.858481,0.837299
134 | 0.543306,3.156417,1.786975
135 | 0.363504,2.800384,1.394307
136 | 0.535059,2.292263,1.308228
137 | 0.713618,3.686940,1.954061
138 | 0.771346,3.280621,2.433738
139 | 0.543992,0.982769,1.420007
140 | 0.327538,3.049196,1.732298
141 | 0.673435,1.028509,1.117894
142 | 0.790048,2.535953,1.121928
143 | 0.652995,1.863358,1.052248
144 | 0.686442,1.832645,0.846608
145 | 0.508688,1.402616,0.332383
146 | 0.469073,2.758476,1.393521
147 | 0.458537,1.728732,0.811129
148 | 0.512026,2.891798,1.572671
149 | 0.260446,2.545887,1.115706
150 | 0.514567,1.812360,1.045068
151 | 0.303983,1.071495,0.737671
152 | 0.785073,1.853721,0.423486
153 | 0.786890,2.892607,1.916911
154 | 0.527549,2.386379,1.864688
155 | 0.871027,2.623073,0.919323
156 | 0.792758,1.133822,0.802184
157 | 0.287012,2.765212,1.237227
158 | 0.991471,1.147160,1.478549
159 | 1.010635,1.351326,1.130389
160 | 0.498485,2.834048,1.618264
161 | 0.499769,1.674872,0.719800
162 | 0.374090,1.192252,1.232728
163 | 0.311496,1.132247,1.155417
164 | 0.800975,1.078114,1.058186
165 | 0.670416,1.042640,1.014992
166 | 0.203875,0.803309,0.966864
167 | 0.322275,2.853453,1.737123
168 | 0.243635,2.530269,1.607557
169 | 0.814055,1.237988,1.056223
170 | 0.188195,2.269979,1.738045
171 | 1.229994,2.832838,1.752615
172 | 0.239432,2.626635,1.411647
173 | 0.596613,2.102660,1.451546
174 | 1.018645,3.839117,2.040820
175 | 0.472394,1.545607,0.636597
176 | 0.568049,1.844661,0.652591
177 | 0.636426,1.120499,0.995884
178 | 0.795271,2.699636,0.369725
179 | 0.224256,1.158848,0.794959
180 | 0.492548,2.640833,1.802385
181 | 0.439187,1.575354,1.054301
182 | 1.076895,1.192625,1.776473
183 | 0.248475,1.230677,0.978322
184 | 0.267990,0.783992,0.676976
185 | 0.602519,2.592819,1.598574
186 | 1.210687,2.576763,0.316156
187 | 1.315860,1.147590,1.084188
188 | 0.442820,2.869822,1.630985
189 | 1.124149,2.686928,0.591529
190 | 0.361749,1.485235,0.770603
191 | 0.707492,1.720605,0.417325
192 | 0.349582,1.541351,0.542061
193 | 0.512217,3.557118,2.467069
194 | 0.682945,1.800466,0.842747
195 | 0.699432,1.773901,1.051560
196 | 0.297333,1.035632,0.889098
197 | 1.451664,1.558393,1.602786
198 | 0.827105,4.602460,1.683862
199 | 2.443390,6.195327,2.995907
200 | 1.533068,5.678340,2.816825
201 | 2.399832,5.752203,2.794411
202 | 3.022819,7.373749,4.032431
203 | 3.172120,5.778477,3.318735
204 | 1.454945,4.995849,2.543083
205 | 3.504108,5.420610,3.727098
206 | 2.484583,4.034882,2.178248
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1 | Eden Deborah Ball,EDB0714,21,23,35,26,29
2 | 


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68 | 0.000000,3.798130,3.877782,3.654227,3.566701,3.547117,3.279088,3.661320,3.884729,0.000000,3.685746,3.644655,3.388751,3.637220,4.556510,2.479338,3.537281,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000
69 | 0.000000,3.834208,3.855587,3.647782,3.608129,3.586329,3.260678,3.671920,3.893390,0.000000,3.717510,3.590635,3.412383,3.595707,4.542313,2.574179,3.750309,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000
70 | 


--------------------------------------------------------------------------------
/Data/HXL0968/Mix/Mix_loss.csv:
--------------------------------------------------------------------------------
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  2 | 0.095081,1.186201,0.909256
  3 | 0.648890,2.359868,1.794773
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149 | 0.655322,3.428484,2.477721
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152 | 0.158103,1.490988,1.281977
153 | 0.150851,1.121243,0.684752
154 | 0.236354,2.321162,1.096226
155 | 0.485074,1.061469,0.930692
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61 | 


--------------------------------------------------------------------------------
/Data/HXL0968/sequence/loss_all.csv:
--------------------------------------------------------------------------------
  1 | 2.146845
  2 | 1.186201
  3 | 2.359868
  4 | 0.593209
  5 | 1.323106
  6 | 0.944320
  7 | 1.653700
  8 | 2.380801
  9 | 2.702028
 10 | 0.806606
 11 | 4.914228
 12 | 2.272713
 13 | 1.123121
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 15 | 1.023036
 16 | 1.035594
 17 | 1.161330
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100 | 0.585780
101 | 3.129261
102 | 0.970096
103 | 2.301792
104 | 2.915215
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108 | 0.616801
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110 | 0.603944
111 | 0.876395
112 | 1.078876
113 | 2.722494
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138 | 0.861090
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141 | 1.065271
142 | 0.992370
143 | 1.665957
144 | 0.579856
145 | 2.365549
146 | 1.008093
147 | 1.014863
148 | 0.662378
149 | 3.428484
150 | 2.043986
151 | 2.060044
152 | 1.490988
153 | 1.121243
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155 | 1.061469
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157 | 0.527323
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160 | 2.604237
161 | 0.838000
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164 | 1.207066
165 | 2.191420
166 | 4.769839
167 | 3.326233
168 | 2.523697
169 | 4.601192
170 | 2.243565
171 | 5.479275
172 | 4.163171
173 | 2.752027
174 | 5.053047
175 | 3.281333
176 | 2.567170
177 | 4.716229
178 | 3.730720
179 | 5.542946
180 | 3.612733
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184 | 4.631653
185 | 4.196799
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190 | 4.519313
191 | 1.018529
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194 | 2.967464
195 | 0.994543
196 | 3.613091
197 | 4.951706
198 | 0.861799
199 | 3.309187
200 | 3.686481
201 | 


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/Data/HXL0968/sequence/predict.csv:
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 1 | 1.021353,3.863104,3.882561,3.958607,3.831611,3.766233,3.242506,3.639476,3.622556,3.454543,3.442358,3.771054,3.740385,3.756640,4.225438,0.000000,3.382467,0.672803,0.000000,0.481527,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000
 2 | 0.992150,3.861946,3.865200,3.990646,3.774280,3.754055,3.219164,3.610235,3.609642,3.435524,3.424215,3.772705,3.639480,3.863664,4.413300,0.000000,3.308348,0.692010,0.000000,0.384970,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000
 3 | 0.978755,3.909735,3.866719,3.981883,3.749239,3.771866,3.250381,3.659336,3.631012,3.391979,3.420304,3.740798,3.660755,3.890721,4.444381,0.000000,3.165415,0.736807,0.000000,0.404349,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000
 4 | 0.985107,3.949340,3.917059,4.048031,3.726866,3.802893,3.130011,3.632675,3.631475,3.375806,3.438028,3.784361,3.706337,4.000522,4.407008,0.000000,3.218600,0.529284,0.000000,0.365565,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000
 5 | 1.018414,3.742547,3.974529,4.035673,3.737584,3.644750,3.151037,3.633781,3.585112,3.548813,3.481657,3.818578,3.579994,3.919886,4.467620,0.000000,3.716956,0.627735,0.000000,0.289615,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000
 6 | 1.048623,3.999886,3.834978,3.930441,3.919278,3.777789,3.299388,3.588835,3.643594,3.534083,3.408609,3.798174,3.902063,3.779293,4.179141,0.000000,3.437214,0.809068,0.000000,0.581414,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000
 7 | 1.002056,3.934444,3.850283,3.934987,3.812664,3.750997,3.231625,3.582107,3.581192,3.463148,3.408788,3.733778,3.740589,3.788850,4.201939,0.000000,3.307237,0.780141,0.000000,0.409518,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000
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 9 | 0.986701,3.838177,3.927950,3.999357,3.698670,3.729807,3.124163,3.668852,3.565676,3.406396,3.475121,3.716553,3.618908,3.867424,4.252235,0.000000,3.377354,0.572707,0.000000,0.307938,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000
10 | 1.034921,3.881539,3.836962,3.901396,3.910458,3.699404,3.333051,3.613227,3.624269,3.642726,3.454158,3.757536,3.903388,3.675205,4.003448,0.000000,3.821350,0.818286,0.000000,0.462238,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000
11 | 1.011737,4.005931,3.687886,3.772647,3.899294,3.748526,3.221902,3.531404,3.532852,3.417393,3.353502,3.590049,3.981483,3.369922,3.444131,0.000000,3.260873,0.725414,0.000000,0.496630,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000
12 | 0.991675,4.000061,3.603976,3.720459,3.852326,3.742167,3.172870,3.484143,3.469471,3.317534,3.342830,3.481604,4.000780,3.202293,3.114227,0.000000,3.145791,0.650725,0.000000,0.411695,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000
13 | 0.960720,4.005167,3.608457,3.777629,3.783051,3.781717,3.152860,3.501666,3.459855,3.217007,3.323614,3.524353,3.939737,3.310477,3.265703,0.000000,2.864652,0.533942,0.000000,0.368878,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000
14 | 0.944216,3.985602,3.751518,3.867909,3.731328,3.806757,3.177346,3.587458,3.523414,3.246280,3.355947,3.588945,3.780058,3.546613,3.767680,0.000000,2.847136,0.620608,0.000000,0.353953,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000
15 | 1.018606,4.059224,3.776443,3.875129,3.868841,3.874183,3.262456,3.612752,3.585095,3.343548,3.404925,3.636073,3.956423,3.490851,3.633374,0.000000,2.988294,0.684832,0.000000,0.521497,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000
16 | 0.991481,3.904361,3.877674,3.996157,3.785841,3.795819,3.226289,3.626583,3.623306,3.410726,3.436762,3.780703,3.651212,3.875310,4.414088,0.000000,3.195939,0.708472,0.000000,0.397555,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000
17 | 1.005349,3.895704,3.919077,4.052360,3.775388,3.784188,3.240271,3.643296,3.658350,3.431288,3.455721,3.839864,3.607924,3.983847,4.649362,0.000000,3.237844,0.709157,0.000000,0.417021,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000
18 | 0.982950,3.892318,3.916244,4.004330,3.702009,3.762263,3.123113,3.672944,3.581359,3.357856,3.453679,3.714206,3.660594,3.904959,4.265806,0.000000,3.223635,0.561077,0.000000,0.328612,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000
19 | 1.003240,3.751017,3.946764,4.019781,3.711996,3.649741,3.139293,3.646170,3.564498,3.496529,3.460514,3.777078,3.565244,3.901708,4.415765,0.000000,3.607374,0.608558,0.000000,0.286234,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000
20 | 1.058523,4.035616,3.839978,3.915104,3.930079,3.786263,3.289952,3.598937,3.625448,3.537093,3.422302,3.753318,3.927413,3.702777,4.025993,0.000000,3.404939,0.815213,0.000000,0.588825,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000
21 | 1.024155,4.059054,3.662281,3.715690,3.923398,3.732618,3.263507,3.515686,3.508395,3.463477,3.365530,3.500302,4.113688,3.267783,3.148751,0.000000,3.393456,0.796856,0.000000,0.460337,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000
22 | 0.920932,3.881647,3.602559,3.693835,3.672633,3.595546,2.999184,3.457793,3.372564,3.260426,3.288347,3.413118,3.809121,3.253750,3.167720,0.000000,3.173179,0.512993,0.000000,0.217225,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000
23 | 0.893009,3.889575,3.582028,3.711729,3.617216,3.604105,2.992426,3.495782,3.364107,3.162666,3.268085,3.388309,3.789941,3.278419,3.203502,0.000000,2.962988,0.462826,0.000000,0.210789,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000
24 | 0.957163,3.984197,3.569811,3.687227,3.776656,3.677828,3.195774,3.540178,3.457099,3.282525,3.326245,3.384599,3.995586,3.221359,3.117741,0.000000,3.143038,0.660885,0.000000,0.340640,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000
25 | 0.971004,3.982261,3.750186,3.865335,3.741079,3.686247,3.058027,3.523073,3.510893,3.409659,3.337764,3.644494,3.842058,3.716347,3.830134,0.000000,3.336036,0.561109,0.000000,0.317199,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000
26 | 1.025560,4.095650,3.696852,3.794533,3.870298,3.798522,3.232050,3.551335,3.543106,3.342581,3.341033,3.570428,4.059145,3.412453,3.439418,0.000000,3.022376,0.691226,0.000000,0.537529,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000
27 | 0.990613,4.007585,3.598422,3.708100,3.888625,3.756062,3.217114,3.519714,3.477999,3.305681,3.348928,3.448061,4.032800,3.216223,3.107862,0.000000,3.122348,0.677444,0.000000,0.405382,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000
28 | 0.994841,4.023759,3.596015,3.667096,3.916731,3.698004,3.228257,3.487254,3.466283,3.415920,3.344986,3.448019,4.080139,3.149001,2.983227,0.000000,3.361796,0.775047,0.000000,0.391484,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000
29 | 0.943517,4.003884,3.529616,3.666178,3.828339,3.699101,3.162305,3.466954,3.413325,3.291696,3.324492,3.411055,4.015189,3.092089,2.881268,0.000000,3.137244,0.637860,0.000000,0.310560,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000
30 | 0.955913,3.957494,3.524610,3.641720,3.853613,3.665723,3.179568,3.468068,3.424764,3.319433,3.322215,3.407515,3.996534,3.054739,2.892003,0.000000,3.228490,0.675048,0.000000,0.337008,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000
31 | 0.942048,4.030100,3.550602,3.692396,3.800424,3.721319,3.142773,3.458073,3.416148,3.220713,3.294264,3.458694,3.986225,3.139618,3.031607,0.000000,2.864939,0.606974,0.000000,0.400109,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000
32 | 0.953442,4.012419,3.545797,3.683366,3.881292,3.747593,3.230253,3.493808,3.445335,3.276329,3.333110,3.434927,4.064898,3.125121,2.938046,0.000000,3.098436,0.655867,0.000000,0.368919,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000
33 | 0.963873,3.966011,3.548871,3.667790,3.849339,3.704697,3.189791,3.491714,3.441710,3.308066,3.342076,3.420926,3.982484,3.104074,2.949799,0.000000,3.186455,0.659915,0.000000,0.330025,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000
34 | 0.967457,4.041298,3.586790,3.718823,3.859823,3.759112,3.205799,3.493428,3.464489,3.319865,3.337294,3.484476,4.031827,3.152919,3.015188,0.000000,3.057961,0.654201,0.000000,0.377887,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000
35 | 0.977740,3.970390,3.550024,3.666295,3.850080,3.707663,3.219186,3.500101,3.441694,3.308980,3.332662,3.400683,4.035014,3.094353,2.887255,0.000000,3.204103,0.675063,0.000000,0.344945,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000
36 | 0.961628,4.018702,3.544646,3.652156,3.833838,3.700763,3.208864,3.491362,3.444807,3.312287,3.338556,3.390887,4.057254,3.087170,2.880722,0.000000,3.161319,0.695673,0.000000,0.346358,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000
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38 | 0.940207,3.942060,3.477244,3.610310,3.794369,3.632494,3.157960,3.483105,3.389114,3.248151,3.316603,3.328781,3.987398,3.013918,2.779700,0.000000,3.186131,0.626150,0.000000,0.273053,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000
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56 | 0.930004,3.969506,3.675488,3.791405,3.746224,3.705850,3.144481,3.486501,3.453759,3.306608,3.294265,3.562973,3.803080,3.426469,3.564689,0.000000,3.005245,0.678697,0.000000,0.321705,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000
57 | 0.988437,3.987913,3.649362,3.742303,3.824953,3.742941,3.218741,3.536811,3.482099,3.319196,3.337039,3.503663,3.979412,3.309838,3.261613,0.000000,3.122545,0.676473,0.000000,0.416729,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000
58 | 0.964814,3.947811,3.609633,3.692238,3.842099,3.692016,3.219527,3.523112,3.458375,3.359187,3.340730,3.436933,3.977926,3.194707,3.117200,0.000000,3.261232,0.720442,0.000000,0.354505,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000
59 | 0.951406,3.844112,3.719784,3.780325,3.707592,3.562424,3.034479,3.493093,3.405504,3.417414,3.304304,3.565603,3.739945,3.470187,3.548338,0.000000,3.460215,0.603255,0.000000,0.226731,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000
60 | 0.990961,4.014637,3.562494,3.651429,3.860107,3.642550,3.243185,3.500847,3.440197,3.386760,3.324883,3.397131,4.074101,3.131624,2.984941,0.000000,3.318586,0.779501,0.000000,0.397689,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000
61 | 


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/Data/TNM0961/Mix/Mix_loss.csv:
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2 | 


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 39 | 1.354133
 40 | 1.381824
 41 | 2.906327
 42 | 1.842138
 43 | 1.598368
 44 | 2.262225
 45 | 4.332440
 46 | 1.883327
 47 | 2.958692
 48 | 3.174113
 49 | 1.697289
 50 | 1.794091
 51 | 2.153440
 52 | 2.872471
 53 | 1.197747
 54 | 3.994123
 55 | 1.654602
 56 | 1.061008
 57 | 2.167167
 58 | 1.427551
 59 | 2.883845
 60 | 2.862745
 61 | 2.541027
 62 | 1.866186
 63 | 3.690288
 64 | 1.922134
 65 | 2.636864
 66 | 2.348984
 67 | 2.538365
 68 | 2.146247
 69 | 2.828341
 70 | 2.299747
 71 | 2.695457
 72 | 2.013554
 73 | 2.414516
 74 | 3.444705
 75 | 3.176922
 76 | 2.625045
 77 | 2.587410
 78 | 2.456060
 79 | 2.886468
 80 | 2.571064
 81 | 3.014831
 82 | 2.071330
 83 | 2.812981
 84 | 2.487272
 85 | 1.503092
 86 | 2.795225
 87 | 2.648737
 88 | 3.741523
 89 | 2.145873
 90 | 3.088301
 91 | 4.536782
 92 | 1.351760
 93 | 1.582683
 94 | 3.066262
 95 | 2.904911
 96 | 2.604849
 97 | 1.994650
 98 | 2.688161
 99 | 3.099766
100 | 2.629049
101 | 2.478611
102 | 1.990343
103 | 2.676070
104 | 1.233281
105 | 1.941874
106 | 1.915017
107 | 2.580693
108 | 1.547029
109 | 2.604049
110 | 1.530803
111 | 2.291285
112 | 3.453852
113 | 2.706180
114 | 2.953707
115 | 3.207953
116 | 2.664980
117 | 3.585810
118 | 2.631690
119 | 2.846537
120 | 2.274245
121 | 2.802389
122 | 2.442836
123 | 2.628993
124 | 2.559466
125 | 2.240563
126 | 1.731077
127 | 2.236430
128 | 1.189214
129 | 1.146544
130 | 5.136215
131 | 1.937750
132 | 1.888285
133 | 3.058445
134 | 2.900367
135 | 2.662617
136 | 2.320288
137 | 3.211763
138 | 1.923082
139 | 3.535846
140 | 2.103223
141 | 1.747242
142 | 2.414748
143 | 2.809297
144 | 2.044799
145 | 1.776047
146 | 1.904608
147 | 2.377200
148 | 3.079978
149 | 2.319573
150 | 3.663630
151 | 2.763165
152 | 1.364061
153 | 2.494825
154 | 1.496643
155 | 2.214049
156 | 2.360350
157 | 1.366906
158 | 2.622181
159 | 1.707023
160 | 1.785666
161 | 1.126143
162 | 3.117364
163 | 2.435893
164 | 3.084778
165 | 1.986574
166 | 2.545659
167 | 1.503291
168 | 2.688449
169 | 1.627909
170 | 3.072935
171 | 2.331198
172 | 3.095373
173 | 3.474593
174 | 1.949861
175 | 2.879849
176 | 2.469307
177 | 2.144482
178 | 2.603240
179 | 2.186644
180 | 2.515623
181 | 4.473803
182 | 4.135414
183 | 3.798131
184 | 2.445604
185 | 2.967211
186 | 3.252048
187 | 1.736554
188 | 2.446766
189 | 2.152098
190 | 2.317692
191 | 1.606927
192 | 3.181798
193 | 1.314831
194 | 1.664405
195 | 3.090111
196 | 3.771095
197 | 4.251308
198 | 4.507872
199 | 6.055544
200 | 4.926491
201 | 5.973510
202 | 5.979363
203 | 5.500611
204 | 3.078389
205 | 8.465838
206 | 4.762858
207 | 3.408236
208 | 5.546807
209 | 7.280873
210 | 7.534455
211 | 4.984678
212 | 5.105161
213 | 8.612507
214 | 5.408289
215 | 2.496051
216 | 6.461075
217 | 1.623474
218 | 3.202813
219 | 6.038536
220 | 2.038394
221 | 2.409251
222 | 5.074200
223 | 4.357726
224 | 1.679289
225 | 2.028130
226 | 3.004541
227 | 3.843406
228 | 5.627711
229 | 4.688851
230 | 3.385185
231 | 


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/Data/readme.txt:
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 1 | I choose three users' data for test. And we considered three types of features which includes action features, action sequence and role features.
 2 | ---------------------------------------
 3 | Users： 'EDB0714','TNM0961' and 'HXL0968'
 4 | Mix: Files mixed with three types of features for the next process.
 5 | Role_feature.csv: the average of actions features of  all users under the role of "ProductionLineWorker".
 6 | 
 7 | ---------------------------------------
 8 | Every user's files:
 9 | 
10 |             * ./ device.csv,email.csv,file.csv,http.csv,logon.csv,psychometric.csv are all extracted from CERT/R4.2 directly.
11 | 
12 |             * new: newly arranged data which came from above.
13 | 
14 |             * feture/sequence:  
15 |                         data_out.csv,data_all.csv, label.csv are total feature data (generated from files in 'new' folder).
16 |                         data_train.csv,test.csv,label_train.csv,label_test.csv are generated from data_all.csv and label_all.csv 
17 |                         for deep model.
18 |                         predict.csv,predict_all.csv are predictions from trained feature model.
19 |                         loss_all.csv,my_loss.csv are two types of loss caculated by trained model.
20 | 
21 |             * Model:
22 |                         Action: deep model and tensorboard file for action sequence.
23 |                         Feature: deep model and tensorboard file for action features. 
24 |                         Role_loss: loss caculated by role features.
25 | 
26 |             * Mix:
27 |                         We spliced these three losses together.
28 | 


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/LICENSE:
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 1 | MIT License
 2 | 
 3 | Copyright (c) 2021 Caocao
 4 | 
 5 | Permission is hereby granted, free of charge, to any person obtaining a copy
 6 | of this software and associated documentation files (the "Software"), to deal
 7 | in the Software without restriction, including without limitation the rights
 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 9 | copies of the Software, and to permit persons to whom the Software is
10 | furnished to do so, subject to the following conditions:
11 | 
12 | The above copyright notice and this permission notice shall be included in all
13 | copies or substantial portions of the Software.
14 | 
15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21 | SOFTWARE.
22 | 


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/README.md:
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 1 | # User-and-Entity-Behavior-Analytics-UEBA
 2 | User and Entity Behavior Analytics by deep learning.  
 3 | Detecting users anomalous behaviors from users' daily records. 
 4 | 
 5 | 内部威胁检测
 6 | ## Details
 7 | All data were extracted from **CERT/R4.2** &nbsp;(*ftp://ftp.sei.cmu.edu/pub/cert-data*)
 8 | 
 9 | **Data**: data for detection.  
10 | 
11 | ## Dependent Libraries
12 | - python 3.63-64-bit 
13 | - numpy 1.16.4
14 | - tensorflow 1.8.0
15 | - keras 2.2.2
16 | - sklearn 0.19.1
17 | 
18 | ## Useage
19 | - Run python files step by step.
20 | - Note that **3-Action_Sequence_Training.py** and **4-Static_Feature_Training.py** need to be run for different users separately, you can find the user_sets and change it. **2-Training_Data_Generating.py** also needs to be run under two feature types, you can find the "types" and change it. 
21 | 
22 | *The provided features and deep learning models in this project are very simple samples, and you can add or create your own features and models based on this project.* : )
23 | 
24 | ## Cite this work
25 | This project is a part of our work that has been published in the ACM/IMS Transactions on Data Science. You can cite this work in your researches. 
26 | 
27 | ACM/IMS Transactions on Data Science, Volume 1, Issue 3 September 2020, Article No.: 16, pp 1–19 https://doi.org/10.1145/3374749
28 | 
29 | [Paper Link](https://dl.acm.org/doi/10.1145/3374749)
30 | 
31 | 


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