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cicids2018_updated.py
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cicids2018_updated.py
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### this code was executed in jupyter notebook and exported as .py files so you may need to modify it to run as python script.
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn import preprocessing
from sklearn.preprocessing import LabelEncoder, Imputer,MinMaxScaler
from sklearn.model_selection import train_test_split,cross_val_score
from sklearn.ensemble import RandomForestClassifier
from sklearn.decomposition import PCA
from sklearn.metrics import confusion_matrix,accuracy_score,precision_recall_fscore_support
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.svm import SVC
from keras.wrappers.scikit_learn import KerasClassifier
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import Adam
from keras.utils.np_utils import to_categorical
from sklearn.ensemble import RandomForestRegressor
df=pd.read_csv('combined2.csv')
df_value=df[' Label'].value_counts()
df[' Label']=df[' Label'].apply({'DoS Hulk':'DoS', 'DoS GoldenEye':'DoS','DoS Slowhttptest':'DoS','DoS
slowloris':'DoS' ,'BENIGN':'BENIGN' ,'DDoS':'DDoS', 'PortScan':'PortScan'}.get)
df2=df.drop_duplicates()
df2_value=df2[' Label'].value_counts()
datatype=df2.dtypes
df2['Flow Bytes/s']=df2['Flow Bytes/s'].astype('float64')
df2[' Flow Packets/s']=df2[' Flow Packets/s'].astype('float64')
NaN_values=df2.isnull().sum()
df2['Flow Bytes/s'].fillna(df2['Flow Bytes/s'].mean(),inplace=True)
print('Datasetin ilk okunduÄŸu hali: \n',df_value)
print('Datasetin ilk (row,Column) sayısı: {} '.format(df.shape))
print('Datasetin Labelindeki DoS daldırılarının birleştirilmesi ve gürültünün azaltılması:\n',df2_value)
print('Datasetin son (row,Column) sayısı: {} '.format(df2.shape))
dataset=pd.read_csv('dataset.csv')
dataset
DoS_df1=dataset[dataset[' Label']=='BENIGN']
DoS_df=DoS_df1.append(dataset[dataset[' Label']=='DoS'])
DoS_df
DDoS_df1=dataset[dataset[' Label']=='BENIGN']
DDoS_df=DDoS_df1.append(dataset[dataset[' Label']=='DDoS'])
DDoS_df
PortScan_df1=dataset[dataset[' Label']=='BENIGN']
PortScan_df=PortScan_df1.append(dataset[dataset[' Label']=='PortScan'])
PortScan_df
NA_df=dataset
NA_df[' Label']=NA_df[' Label'].apply({'DoS':'Anormal','BENIGN':'Normal' ,'DDoS':'Anormal', 'PortScan':'Anormal'}.get)
NA_df
def train_test_dataset(df):
labelencoder = LabelEncoder()
df.iloc[:, -1] = labelencoder.fit_transform(df.iloc[:, -1])
X = df.drop([' Label'],axis=1)
y = df.iloc[:, -1].values.reshape(-1,1)
y=np.ravel(y)
X_train, X_test, y_train, y_test = train_test_split(X,y, train_size = 0.7, test_size = 0.3, random_state = 0, stratify = y)
return X_train, X_test, y_train, y_test
def RandomForest(X_train, X_test, y_train, y_test):
rf = RandomForestClassifier(random_state = 0)
imputer = Imputer(missing_values="NaN", strategy = "mean")
imputer = imputer.fit(X_train)
X_train = imputer.transform(X_train)
X_test = imputer.transform(X_test)
rf.fit(X_train,y_train)
rf_score=rf.score(X_test,y_test)
y_predict=rf.predict(X_test)
y_true=y_test
print('Random Forest Accuracy:'+ str(rf_score))
precision,recall,fscore,none= precision_recall_fscore_support(y_true, y_predict, average='weighted')
print('Random Forest precision_recall_fscore:'+(str(precision))+(str(recall))+(str(fscore)))
cm=confusion_matrix(y_true,y_predict)
f,ax=plt.subplots(figsize=(5,5))
sns.heatmap(cm,annot=True,linewidth=0.5,linecolor="red",fmt=".0f",ax=ax)
plt.xlabel("y_pred")
plt.ylabel("y_true")
plt.show()
return rf_score,precision,recall,fscore,none
def DecisionTree(X_train, X_test, y_train, y_test):
dt = DecisionTreeClassifier(random_state = 0)
imputer = Imputer(missing_values="NaN", strategy = "mean")
imputer = imputer.fit(X_train)
X_train = imputer.transform(X_train)
X_test = imputer.transform(X_test)
dt.fit(X_train, y_train)
score=dt.score(X_test,y_test)
print('Decision Tree Accuracy:'+ str(score))
y_predict=dt.predict(X_test)
y_true=y_test
precision,recall,fscore,none= precision_recall_fscore_support(y_true, y_predict, average='weighted')
print('Decision Tree precision_recall_fscore:'+(str(precision))+(str(recall))+(str(fscore)))
cm=confusion_matrix(y_true,y_predict)
f,ax=plt.subplots(figsize=(5,5))
sns.heatmap(cm,annot=True,linewidth=0.5,linecolor="red",fmt=".0f",ax=ax)
plt.xlabel("y_pred")
plt.ylabel("y_true")
plt.show()
return score,precision,recall,fscore,none
def kNN(X_train, X_test, y_train, y_test):
knn=KNeighborsClassifier(n_neighbors=5)
imputer = Imputer(missing_values="NaN", strategy = "mean")
imputer = imputer.fit(X_train)
X_train = imputer.transform(X_train)
X_test = imputer.transform(X_test)
knn.fit(X_train,y_train)
prediction=knn.predict(X_test)
score=knn.score(X_test,y_test)
print("5 nn score:"+ str(score))
y_predict=knn.predict(X_test)
y_true=y_test
precision,recall,fscore,none= precision_recall_fscore_support(y_true, y_predict, average='weighted')
print('5nn precision_recall_fscore:'+(str(precision))+(str(recall))+(str(fscore)))
cm=confusion_matrix(y_true,y_predict)
f,ax=plt.subplots(figsize=(5,5))
sns.heatmap(cm,annot=True,linewidth=0.5,linecolor="red",fmt=".0f",ax=ax)
plt.xlabel("y_pred")
plt.ylabel("y_true")
plt.show()
return score,precision,recall,fscore,none
def SVM(X_train, X_test, y_train, y_test):
svclassifier = SVC(kernel='linear')
imputer = Imputer(missing_values="NaN", strategy = "mean")
imputer = imputer.fit(X_train)
X_train = imputer.transform(X_train)
X_test = imputer.transform(X_test)
svclassifier.fit(X_train, y_train)
print("SVM Classification Accuracy:"+ str(svclassifier.score(X_test,y_test)))
y_predict = svclassifier.predict(X_test)
y_true=y_test
cm=confusion_matrix(y_true,y_predict)
f,ax=plt.subplots(figsize=(5,5))
sns.heatmap(cm,annot=True,linewidth=0.5,linecolor="red",fmt=".0f",ax=ax)
plt.xlabel("y_pred")
plt.ylabel("y_true")
plt.show()
def build_classifier(X_train):
def bm():
classifier = Sequential()
classifier.add(Dense(units = 80, kernel_initializer = 'uniform', activation = 'relu', input_dim = X_train.shape[1]))
classifier.add(Dense(units = 25, kernel_initializer = 'uniform', activation = 'relu'))
classifier.add(Dense(units = 2, kernel_initializer = 'uniform', activation = 'softmax'))
lr=.003
adam0=Adam(lr=lr)
classifier.compile(optimizer =adam0, loss = 'categorical_crossentropy', metrics = ['accuracy'])
return classifier
return bm
def ANN(X_train, X_test, y_train, y_test):
y_ = to_categorical(y_train)
y_t=to_categorical(y_test)
estimator = KerasClassifier(build_fn = build_classifier(X_train), epochs = 5)
accuracies = cross_val_score(estimator, X = X_train, y = y_, cv = 3)
mean = accuracies.mean()
variance = accuracies.std()
print("Accuracy mean: "+ str(mean))
print("Accuracy variance: "+ str(variance))
def feature_selection(df):
feature=(df.drop([' Label'],axis=1)).columns.values
labelencoder = LabelEncoder()
df.iloc[:, -1] = labelencoder.fit_transform(df.iloc[:, -1])
X = df.drop([' Label'],axis=1)
Y = df.iloc[:, -1].values.reshape(-1,1)
Y=np.ravel(Y)
imputer = Imputer(missing_values="NaN", strategy = "mean")
imputer = imputer.fit(X)
X = imputer.transform(X)
rf = RandomForestRegressor()
rf.fit(X, Y)
print ("Features sorted by their score:")
print (sorted(zip(map(lambda x: round(x, 4), rf.feature_importances_), feature), reverse=True))
feature_selection(dataset)
DoSX_train, DoSX_test, DoSy_train, DoSy_test=train_test_dataset(DoS_df)
DDoSX_train, DDoSX_test, DDoSy_train, DDoSy_test=train_test_dataset(DDoS_df)
PS_X_train,PS_X_test,PS_y_train, PS_y_test=train_test_dataset(PortScan_df)
NA_X_train, NA_X_test, NA_y_train, NA_y_test=train_test_dataset(NA_df)
dosrf_score,dosrf_precision,dosrf_recall,dosrf_fscore,none=RandomForest(DoSX_train, DoSX_test, DoSy_train, DoSy_test)
dosdt_score,dosdt_precision,dosdt_recall,dosdt_fscore,none=DecisionTree(DoSX_train, DoSX_test, DoSy_train, DoSy_test)
dosKnn_score,dosKnn_precision,dosKnn_recall,dosKnn_fscore,none=kNN(DoSX_train, DoSX_test, DoSy_train, DoSy_test)
SVM(DoSX_train, DoSX_test, DoSy_train, DoSy_test)
ANN(DoSX_train, DoSX_test, DoSy_train, DoSy_test)
labels=np.unique(DoSy_train)
print(labels)
psrf_score,psrf_precision,psrf_recall,psrf_fscore,none=RandomForest(PS_X_train,PS_X_test,PS_y_train, PS_y_test)
psdt_score,psdt_precision,psdt_recall,psdt_fscore,none=DecisionTree(PS_X_train,PS_X_test,PS_y_train, PS_y_test)
psKnn_score,psKnn_precision,psKnn_recall,psKnn_fscore,none=kNN(PS_X_train,PS_X_test,PS_y_train, PS_y_test)
SVM(PS_X_train,PS_X_test,PS_y_train, PS_y_test)
ANN(PS_X_train,PS_X_test,PS_y_train, PS_y_test)
ddosrf_score,ddosrf_precision,ddosrf_recall,ddosrf_fscore,none=RandomForest(DDoSX_train, DDoSX_test, DDoSy_train, DDoSy_test)
ddosdt_score,ddosdt_precision,ddosdt_recall,ddosdt_fscore,none=DecisionTree(DDoSX_train, DDoSX_test, DDoSy_train, DDoSy_test)
ddosKnn_score,ddosKnn_precision,ddosKnn_recall,ddosKnn_fscore,none=kNN(DDoSX_train, DDoSX_test, DDoSy_train, DDoSy_test)
SVM(DDoSX_train, DDoSX_test, DDoSy_train, DDoSy_test)
ANN(DDoSX_train, DDoSX_test, DDoSy_train, DDoSy_test)
narf_score,narf_precision,narf_recall,narf_fscore,none=RandomForest(NA_X_train, NA_X_test, NA_y_train,
NA_y_test)
nadt_score,nadt_precision,nadt_recall,nadt_fscore,none=DecisionTree(NA_X_train, NA_X_test, NA_y_train,
NA_y_test)
naKnn_score,naKnn_precision,naKnn_recall,naKnn_fscore,none=kNN(NA_X_train, NA_X_test, NA_y_train, NA_y_test)
SVM(NA_X_train, NA_X_test, NA_y_train, NA_y_test)
ANN(NA_X_train, NA_X_test, NA_y_train, NA_y_test)
d={'Algoritmalar': ["Random Forest", "Decision Tree","KNN","ANN"],
'DoS accuracy': [dosrf_score,dosdt_score,dosKnn_score,0.7636],
'DDoS accuracy': [ddosrf_score, ddosdt_score,ddosKnn_score,0.8307],
'Port Scan accuracy':[psrf_score,psdt_score,psKnn_score,0.8738],
'Normal/Anormal accuracy':[narf_score,nadt_score,naKnn_score,0.6034],
}
dataframe= pd.DataFrame(data=d)
dataframe