Python

Question

我创建了一个具有 4 个分类特征和二进制结果的人工神经网络，1 表示可疑，0 表示不可疑：

  ParentPath                                  ParentExe
0   C:\Program Files (x86)\Wireless AutoSwitch  wrlssw.exe
1   C:\Program Files (x86)\Wireless AutoSwitch  WrlsAutoSW.exs
2   C:\Program Files (x86)\Wireless AutoSwitch  WrlsAutoSW.exs
3   C:\Windows\System32                         svchost.exe
4   C:\Program Files (x86)\Wireless AutoSwitch  WrlsAutoSW.exs

ChildPath                                   ChildExe    Suspicious
C:\Windows\System32                         conhost.exe  0
C:\Program Files (x86)\Wireless AutoSwitch  wrlssw.exe   0 
C:\Program Files (x86)\Wireless AutoSwitch  wrlssw.exe   0
C:\Program Files\Common Files               OfficeC2RClient.exe  0
C:\Program Files (x86)\Wireless AutoSwitch  wrlssw.exe  1
C:\Program Files (x86)\Wireless AutoSwitch  wrlssw.exe  0

我使用 sklearn 进行标签编码，并对数据进行一种热编码：

#Import the dataset
X = DBF2.iloc[:, 0:4].values
#X = DBF2[['ParentProcess', 'ChildProcess']]
y = DBF2.iloc[:, 4].values#.ravel()

#Encoding categorical data
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
#Label Encode Parent Path
labelencoder_X_1 = LabelEncoder()
X[:, 0] = labelencoder_X_1.fit_transform(X[:, 0])
#Label Encode Parent Exe
labelencoder_X_2 = LabelEncoder()
X[:, 1] = labelencoder_X_2.fit_transform(X[:, 1])
#Label Encode Child Path
labelencoder_X_3 = LabelEncoder()
X[:, 2] = labelencoder_X_3.fit_transform(X[:, 2])
#Label Encode Child Exe
labelencoder_X_4 = LabelEncoder()
X[:, 3] = labelencoder_X_4.fit_transform(X[:, 3])

#Create dummy variables
onehotencoder = OneHotEncoder(categorical_features = [0,1,2,3])
X = onehotencoder.fit_transform(X)

我已将数据拆分为训练集和测试集，并运行在我的带有 nvidia 1080 的 gpu 盒子上。我已经调整了超参数，现在准备好使用在一次测试一个测试样本的生产环境。假设我只想测试一个样本：

   ParentPath            ParentExe     ChildPath           ChildExe
0  C:\Windows\Malicious  badscipt.exe  C:\Windows\System   cmd.exe

我运行遇到的问题是训练集看到了正常的ChildPath "C:\Windows\System"和ChildExe "cmd.exe"，但是训练集没有看到ParentPath "C:\Windows\Malicous" 或 ParentExe "badscipt.exe" 所以这些都没有被标记或一个热编码。我的大问题是如何处理其中一部分尚未训练的测试功能？

我看过使用特征散列的示例，但我不确定如何应用它或者是否能解决这个问题。任何帮助或指示将不胜感激。

Answer 1

#Create data frame with malicous test
testmalicious = {'ParentProcess': ['ParentProcess': ['C:\Windows\System32\services.exe'], 'ChildProcess': ['C:\Windows\System32\svch0st.exe'], 'Suspicous': [1]}
testmaliciousdf = pd.DataFrame(data=testmalicious)
testmaliciousdf = testmaliciousdf[['ParentProcess', 'ChildProcess', 'Suspicous']]
#Add the malicious to the end of dataframe
DBF1 = DBF2.append(testmaliciousdf)
DBF2 = DBF1.reset_index(drop=True)
#Location where mal_array sample is located - after label and one hot encoded pull out of training set
mal_array = X[368827:368828]
#Remove the last line of the array from training set
X=X[:-1]
#Remove the last line of the array from the y data
y=y[:-1]
#At the end test if suspicious or not
new_prediction = classifier.predict(sc.transform(mal_array))
new_prediction = (new_prediction > 0.5)
new_prediction

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