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nids_csv_updated.py
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nids_csv_updated.py
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import numpy as np
import sys
from sklearn.metrics import accuracy_score, confusion_matrix
import pandas as pd
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import MinMaxScaler
import sklearn
from sklearn.neighbors import KNeighborsClassifier
import os
#from google.colab import drive
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import Normalizer
import tensorflow as tf
import pickle
#Uploaded_files\fs_test.csv
path='Uploaded_files/'
val=sys.argv[1]
path+=sys.argv[2];
#path='/content/gdrive/My Drive/fs_test.csv'
f=open(path)
data_Validate=pd.read_csv(f)
columns = (['protocol_type','service','flag','logged_in','count','srv_serror_rate','srv_rerror_rate','same_srv_rate','diff_srv_rate','dst_host_count','dst_host_srv_count','dst_host_same_srv_rate','dst_host_diff_srv_rate','dst_host_same_src_port_rate','dst_host_serror_rate','dst_host_rerror_rate'])
data_Validate.columns=columns
protocol_type_le = LabelEncoder()
service_le = LabelEncoder()
flag_le = LabelEncoder()
data_Validate['protocol_type'] = protocol_type_le.fit_transform(data_Validate['protocol_type'])
data_Validate['service'] = service_le.fit_transform(data_Validate['service'])
data_Validate['flag'] = flag_le.fit_transform(data_Validate['flag'])
df_validate=data_Validate.copy(deep=True)
x_validate=df_validate.copy(deep=True)
label_encoder = LabelEncoder()
scaler=MinMaxScaler()
x1=x_validate.copy(deep=True)
scaler=MinMaxScaler()
scaler.fit(x1)
scaled_data=scaler.transform(x1)
scaled_data=pd.DataFrame(scaled_data)
scaled_data.columns= x1.columns
x_validate=pd.DataFrame(scaled_data)
print(x_validate.shape)
if(val=='knn'):
knn_bin = pickle.load(open('knn_binary_class.sav', 'rb'))
knn_multi = pickle.load(open('knn_multi_class.sav', 'rb'))
x_predict_bin=knn_bin.predict(x_validate)
x_predict_multi=knn_multi.predict(x_validate)
l=[]
for i in x_predict_bin:
if(i == 0):
l.append('Normal')
else:
l.append('Attack')
l=np.array(l)
df_validate['binary class']=l
df_validate['multi class']=x_predict_multi
df_validate.to_csv(path,index=False)
elif(val=='rf'):
rf_bin = pickle.load(open('random_forest_binary_class.sav', 'rb'))
rf_multi = pickle.load(open('random_forest_multi_class.sav', 'rb'))
x_predict_bin=rf_bin.predict(x_validate)
x_predict_multi=rf_multi.predict(x_validate)
l=[]
for i in x_predict_bin:
if(i == 0):
l.append('Normal')
else:
l.append('Attack')
l=np.array(l)
df_validate['binary class']=l
df_validate['multi class']=x_predict_multi
df_validate.to_csv(path,index=False)
elif(val=='cnn'):
x_validate=df_validate.iloc[:,0:16]
scaler = Normalizer().fit(x_validate)
x_validate = scaler.transform(x_validate)
np.set_printoptions(precision=3)
cnn_bin=tf.keras.models.load_model('latest_cnn_bin.h5')
cnn_multi=tf.keras.models.load_model('latest_cnn_multiclass.h5')
x_validate = np.reshape(x_validate, (x_validate.shape[0],1,x_validate.shape[1]))
x_predict_bin=cnn_bin.predict(x_validate,verbose=False)
x_validate=df_validate.iloc[:,0:16]
scaler = Normalizer().fit(x_validate)
x_validate = scaler.transform(x_validate)
np.set_printoptions(precision=3)
x_validate = np.reshape(x_validate, (x_validate.shape[0],x_validate.shape[1],1))
x_predict_multi=cnn_multi.predict(x_validate,verbose=False)
l=[]
l1=[]
for i in x_predict_multi:
te=[]
for j in i:
te.append(round(j))
l.append(te)
res=[]
for i in l:
if(i[0]==1):
res.append('Dos')
elif(i[1]==1):
res.append('Normal')
elif(i[2]==1):
res.append('Probe')
elif(i[3]==1):
res.append('R2L')
elif(i[4]==1):
res.append('U2R')
else:
res.append('Normal')
l=np.array(res)
l1=[]
for i in x_predict_bin:
for j in i:
l1.append(round(j))
res=[]
for i in l1:
if(i==0):
res.append('Normal')
else:
res.append('Attack')
l1=np.array(res)
df_validate['binary class']=l1
print(l)
df_validate['multi class']=l
df_validate.to_csv(path,index=False)
elif(val=='lstm'):
lstm_bin=tf.keras.models.load_model('lstm_latest_bin.h5')
lstm_multi=tf.keras.models.load_model('lstm_latest_multiclass.h5')
x_validate=df_validate.iloc[:,0:16]
scaler = Normalizer().fit(x_validate)
x_validate = scaler.transform(x_validate)
np.set_printoptions(precision=3)
x_validate = np.reshape(x_validate, (x_validate.shape[0],1, x_validate.shape[1]))
x_predict_bin=lstm_bin.predict(x_validate,verbose=False)
x_predict_multi=lstm_multi.predict(x_validate,verbose=False)
l=[]
l1=[]
for i in x_predict_multi:
te=[]
for j in i:
te.append(round(j))
l.append(te)
res=[]
for i in l:
if(i[0]==1):
res.append('Dos')
elif(i[1]==1):
res.append('Normal')
elif(i[2]==1):
res.append('Probe')
elif(i[3]==1):
res.append('R2L')
elif(i[4]==1):
res.append('U2R')
else:
res.append('Normal')
l=np.array(res)
l1=[]
for i in x_predict_bin:
for j in i:
l1.append(round(j))
res=[]
for i in l1:
if(i==0):
res.append('Normal')
else:
res.append('Attack')
l1=np.array(res)
df_validate['binary class']=l1
df_validate['multi class']=l
df_validate.to_csv(path,index=False)
print('completed')