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csv2image.py
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csv2image.py
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# -*- coding: utf-8 -*-
"""
Created on Fri Jan 17 12:13:31 2020
@author: Faisal Hussain
@email: [email protected]
--- Paper Reference ----
@inproceedings{hussain2020iot,
title={IoT DoS and DDoS Attack Detection using ResNet},
author={Hussain, Faisal and Abbas, Syed Ghazanfar and Husnain, Muhammad and Fayyaz, Ubaid U and Shahzad, Farrukh and Shah, Ghalib A},
booktitle={2020 IEEE 23rd International Multitopic Conference (INMIC)},
pages={1--6},
year={2020},
organization={IEEE}
}
"""
import os
import pandas as pd
import numpy as np
import cv2
from sklearn import preprocessing
path = 'E:\\Faisal\\IDS Datasets\\CIC-DDoS-19\\CSVs\\CSV-01-12\\01-12\\'
#path = 'E:\\Faisal\\IDS Datasets\\CIC-DDoS-19\\CSVs\\CSV-03-11\\03-11-2\\'
listOfFiles = os.listdir(path)
#print(listOfFiles)
count = 0
dstpath = 'D:\\CICDDoS19_Scaled\\'
dstpath2 = dstpath + 'Normal\\' #All normal Images will be saved in this folder
#os.mkdir(dstpath2)
for fname in listOfFiles:
print(fname+' dataframe')
#count = count + 1
#dst = dstpath + fname + 'Imgs\\'
dstpath3 = dstpath + fname + 'Imgs\\'
os.mkdir(dstpath3)
print('--- Reading File into DataFrame ---')
df = pd.read_csv(path + fname)
print('--- Dropping Useless Features')
df.drop(labels=['Unnamed: 0', 'Flow ID', ' Source IP', ' Source Port', ' Destination IP', ' Destination Port', ' Protocol',
' Timestamp', 'SimillarHTTP', ' Bwd PSH Flags', ' Fwd URG Flags', ' Bwd URG Flags', 'FIN Flag Count',
' PSH Flag Count', ' ECE Flag Count', 'Fwd Avg Bytes/Bulk', ' Fwd Avg Packets/Bulk', ' Fwd Avg Bulk Rate',
' Bwd Avg Bytes/Bulk', ' Bwd Avg Packets/Bulk', 'Bwd Avg Bulk Rate', ' RST Flag Count', ' Fwd Header Length.1',
'Subflow Fwd Packets', ' Subflow Fwd Bytes', ' Subflow Bwd Packets', ' Subflow Bwd Bytes'], axis=1, inplace=True)
print('df.shape: ', df.shape)
#Replace all values = Infinity with NAN
df = df.replace('Infinity', np.NaN)
# drop rows with missing values
df.dropna(inplace=True)
print('df.shape After dropping: ', df.shape)
print('Separating Normal & Attack Traffic')
normal = df[df.loc[:, ' Label'] == 'BENIGN']
attack = df[df.loc[:, ' Label'] != 'BENIGN']
print('Normalizing the Normal Traffic')
normal.drop([' Label'], axis=1, inplace=True)
#print(normal.shape)
minmax_scale = preprocessing.MinMaxScaler(feature_range=(0, 255))
normal1 = minmax_scale.fit_transform(normal)
normal = pd.DataFrame(normal1)
print('Normalizing the Attack Traffic')
attack.drop([' Label'], axis=1, inplace=True)
#print(normal.shape)
minmax_scale = preprocessing.MinMaxScaler(feature_range=(0, 255))
attack1 = minmax_scale.fit_transform(attack)
attack = pd.DataFrame(attack1)
print('Checking size of Normal & Attack Traffic')
r1 = int(len(normal)/180)
r2 = int(len(attack)/180)
print('--- Generating Normal Images ----')
for i in range(0,r1):
p = i * 180
q = p + 60
img = np.zeros([60,60,3])
img[:,:,0] = normal.iloc[p:q, 0:60].values
img[:,:,1] = normal.iloc[q:q+60, 0:60].values
img[:,:,2] = normal.iloc[q+60:q+120, 0:60].values
imgName = dstpath2 + fname + str(i) +'_normal.png'
cv2.imwrite(imgName, img)
print(i)
print('--- Generating Attack Images ----')
for i in range(0,r2):
p = i * 180
q = p + 60
img = np.zeros([60,60,3])
img[:,:,0] = attack.iloc[p:q, 0:60].values
img[:,:,1] = attack.iloc[q:q+60, 0:60].values
img[:,:,2] = attack.iloc[q+60:q+120, 0:60].values
imgName = dstpath3 + fname + str(i) +'_attack.png'
cv2.imwrite(imgName, img)
print(i)
print('--- done ---')
print('--- All Done ---')