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preprocess.py
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preprocess.py
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import header
import importlib
importlib.reload(header) # For reloading after making changes
from header import *
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from scipy import stats
# Feature Importance
from sklearn import datasets
from sklearn import metrics
from sklearn.ensemble import ExtraTreesClassifier
USE_THREE_BOTS = 0
DEBUG = 1
network_traffic = 1
CAT_FEATURES = 0
CTU_NERIS = 0
USE_ONE_BOT = 1
USE_ALL_BOTS = 0
USE_FOUR_BOTS = 0
REMOVE_SKEW = 0
USE_FEATURE_REDUCTION = 0
def top_features(data, classifier=""):
X = data.drop(["Label"], axis=1).values
y = data["Label"].values
clf = XGBClassifier()
# if(classifier=='XGB'):
print("Evaluation ---->> XBG")
clf = XGBClassifier()
# if ALL_CLASSIFIERS:
# if (classifier=='DT'):
# print('Evaluation ---->> DT')
# clf = DecisionTreeClassifier()
# elif (classifier=='RF'):
# print('Evaluation ---->> RF')
# clf=RandomForestClassifier(n_estimators=100)
clf.fit(X, y)
importance = clf.feature_importances_
importance = [importance.tolist()]
print(importance)
important_features_dict = {}
# important_list = list(map(lambda el:[el], importance))
dictlist = []
print(importance)
importance_df = pd.DataFrame(
importance, columns=data.drop(["Label"], axis=1).columns
)
print(importance_df)
for x, i in enumerate(importance_df.columns):
important_features_dict[x] = i
important_features_list = sorted(
important_features_dict, key=important_features_dict.get, reverse=True
)
print("Most important features: %s" % important_features_list)
importance_df = importance_df.loc[:, (importance_df != 0).any(axis=0)]
print(len(importance_df.columns))
return importance_df.columns
# # # summarize feature importance
# # for i,v in enumerate(importance):
# # print('Feature: %0d, Score: %.5f' % (i,v))
# # # plot feature importance
# pyplot.bar([x for x in range(len(importance))], importance)
# pyplot.show()
def prepare_ISCX_2014_data(PATH="", INPUT_FILE_NAME=""):
# =============================================================================================
# Read Data
# =============================================================================================
data = pd.read_csv(str(PATH) + str(INPUT_FILE_NAME), low_memory=False)
print(
"Processing File: " + str(INPUT_FILE_NAME) + " DATA shape: " + str(data.shape)
)
# =============================================================================================
# Drop all categorical features
# =============================================================================================
if network_traffic:
data = data.drop(
["Src IP", "Src Port", "Dst IP", "Dst Port", "Timestamp", "Protocol"],
axis=1,
).copy()
if DEBUG:
print("data_df after removing categorical features")
print(data.shape)
# =============================================================================================
# Choose only Flow ID and Label columns for labeling purposes in another data frame
# =============================================================================================
data_df = data[["Flow ID", "Label"]].copy()
# =============================================================================================
# remove any columns with all values = Zero
# =============================================================================================
data = data.loc[:, (data != 0).any(axis=0)]
# IRC = data_df['Flow ID'].str.contains('192.168.2.112-131.202.243.84|192.168.5.122-198.164.30.2|192.168.2.110-192.168.5.122|192.168.4.118-192.168.5.122|192.168.2.113-192.168.5.122|192.168.1.103-192.168.5.122|192.168.4.120-192.168.5.122|192.168.2.112-192.168.2.110|192.168.2.112-192.168.4.120|192.168.2.112-192.168.1.103|192.168.2.112-192.168.2.113|192.168.2.112-192.168.4.118|192.168.2.112-192.168.2.109|192.168.2.112-192.168.2.105|192.168.1.105-192.168.5.122')
IRC_1 = data_df["Flow ID"].str.contains("192.168.2.112-131.202.243.84")
IRC_2 = data_df["Flow ID"].str.contains("192.168.5.122-198.164.30.2")
IRC_3 = data_df["Flow ID"].str.contains("192.168.2.110-192.168.5.122")
IRC_4 = data_df["Flow ID"].str.contains("192.168.4.118-192.168.5.122")
IRC_5 = data_df["Flow ID"].str.contains("192.168.2.113-192.168.5.122")
IRC_6 = data_df["Flow ID"].str.contains("192.168.1.103-192.168.5.122")
IRC_7 = data_df["Flow ID"].str.contains("192.168.4.120-192.168.5.122")
IRC_8 = data_df["Flow ID"].str.contains("192.168.2.112-192.168.2.110")
IRC_9 = data_df["Flow ID"].str.contains("192.168.2.112-192.168.4.120")
IRC_10 = data_df["Flow ID"].str.contains("192.168.2.112-192.168.1.103")
IRC_11 = data_df["Flow ID"].str.contains("192.168.2.112-192.168.2.113")
IRC_12 = data_df["Flow ID"].str.contains("192.168.2.112-192.168.4.118")
IRC_13 = data_df["Flow ID"].str.contains("192.168.2.112-192.168.2.109")
IRC_14 = data_df["Flow ID"].str.contains("192.168.2.112-192.168.2.105")
IRC_15 = data_df["Flow ID"].str.contains("192.168.1.105-192.168.5.122")
Neris = data_df["Flow ID"].str.contains("147.32.84.180")
RBot = data_df["Flow ID"].str.contains("147.32.84.170")
Menti = data_df["Flow ID"].str.contains("147.32.84.150")
Sogou = data_df["Flow ID"].str.contains("147.32.84.140")
Murlo = data_df["Flow ID"].str.contains("147.32.84.130")
Virut = data_df["Flow ID"].str.contains("147.32.84.160")
Black_hole_1 = data_df["Flow ID"].str.contains("10.0.2.15")
Black_hole_2 = data_df["Flow ID"].str.contains("192.168.106.141")
Black_hole_3 = data_df["Flow ID"].str.contains("192.168.106.131")
TBot_1 = data_df["Flow ID"].str.contains("172.16.253.130")
TBot_2 = data_df["Flow ID"].str.contains("172.16.253.131")
TBot_3 = data_df["Flow ID"].str.contains("172.16.253.129")
TBot_4 = data_df["Flow ID"].str.contains("172.16.253.240")
Weasel_master = data_df["Flow ID"].str.contains("74.78.117.238")
Weasel_bot = data_df["Flow ID"].str.contains("158.65.110.24")
Zeus_1 = data_df["Flow ID"].str.contains("192.168.3.35")
Zeus_2 = data_df["Flow ID"].str.contains("192.168.3.25")
Zeus_3 = data_df["Flow ID"].str.contains("192.168.3.65")
bin_Zeus = data_df["Flow ID"].str.contains("172.29.0.116")
Osx_trojan = data_df["Flow ID"].str.contains("172.29.0.109")
Zero_access_1 = data_df["Flow ID"].str.contains("172.16.253.132")
Zero_access_2 = data_df["Flow ID"].str.contains("192.168.248.165")
Smoke_bot = data_df["Flow ID"].str.contains("10.37.130.4")
if CTU_NERIS:
CTU_neris = data_df["Flow ID"].str.contains("147.32.84.165")
# SARUMAN1 = data_df['Flow ID'].str.contains('147.32.84.191')
# SARUMAN2 = data_df['Flow ID'].str.contains('147.32.84.192')
# SARUMAN3 = data_df['Flow ID'].str.contains('147.32.84.193')
# SARUMAN4 = data_df['Flow ID'].str.contains('147.32.84.204')
# SARUMAN5 = data_df['Flow ID'].str.contains('147.32.84.205')
# SARUMAN6 = data_df['Flow ID'].str.contains('147.32.84.206')
# SARUMAN7 = data_df['Flow ID'].str.contains('147.32.84.207')
# SARUMAN8 = data_df['Flow ID'].str.contains('147.32.84.208')
# SARUMAN9 = data_df['Flow ID'].str.contains('147.32.84.209')
indx_IRC_1 = [i for i, x in enumerate(IRC_1) if x]
indx_IRC_2 = [i for i, x in enumerate(IRC_2) if x]
indx_IRC_3 = [i for i, x in enumerate(IRC_3) if x]
indx_IRC_4 = [i for i, x in enumerate(IRC_4) if x]
indx_IRC_5 = [i for i, x in enumerate(IRC_5) if x]
indx_IRC_6 = [i for i, x in enumerate(IRC_6) if x]
indx_IRC_7 = [i for i, x in enumerate(IRC_7) if x]
indx_IRC_8 = [i for i, x in enumerate(IRC_8) if x]
indx_IRC_9 = [i for i, x in enumerate(IRC_9) if x]
indx_IRC_10 = [i for i, x in enumerate(IRC_10) if x]
indx_IRC_11 = [i for i, x in enumerate(IRC_11) if x]
indx_IRC_12 = [i for i, x in enumerate(IRC_12) if x]
indx_IRC_13 = [i for i, x in enumerate(IRC_13) if x]
indx_IRC_14 = [i for i, x in enumerate(IRC_14) if x]
indx_IRC_15 = [i for i, x in enumerate(IRC_15) if x]
indx_Neris = [i for i, x in enumerate(Neris) if x]
indx_RBot = [i for i, x in enumerate(RBot) if x]
indx_Menti = [i for i, x in enumerate(Menti) if x]
indx_Sogou = [i for i, x in enumerate(Sogou) if x]
indx_Murlo = [i for i, x in enumerate(Murlo) if x]
indx_Virut = [i for i, x in enumerate(Virut) if x]
indx_Black_hole_1 = [i for i, x in enumerate(Black_hole_1) if x]
indx_Black_hole_2 = [i for i, x in enumerate(Black_hole_2) if x]
indx_Black_hole_3 = [i for i, x in enumerate(Black_hole_3) if x]
indx_TBot_1 = [i for i, x in enumerate(TBot_1) if x]
indx_TBot_2 = [i for i, x in enumerate(TBot_2) if x]
indx_TBot_3 = [i for i, x in enumerate(TBot_3) if x]
indx_TBot_4 = [i for i, x in enumerate(TBot_4) if x]
indx_Weasel_master = [i for i, x in enumerate(Weasel_master) if x]
indx_Weasel_bot = [i for i, x in enumerate(Weasel_bot) if x]
indx_Zeus_1 = [i for i, x in enumerate(Zeus_1) if x]
indx_Zeus_2 = [i for i, x in enumerate(Zeus_2) if x]
indx_Zeus_3 = [i for i, x in enumerate(Zeus_3) if x]
indx_bin_Zeus = [i for i, x in enumerate(bin_Zeus) if x]
indx_Osx_trojan = [i for i, x in enumerate(Osx_trojan) if x]
indx_Zero_access_1 = [i for i, x in enumerate(Zero_access_1) if x]
indx_Zero_access_2 = [i for i, x in enumerate(Zero_access_2) if x]
indx_Smoke_bot = [i for i, x in enumerate(Smoke_bot) if x]
indx_Zero_access_1 = [i for i, x in enumerate(Zero_access_1) if x]
indx_Zero_access_2 = [i for i, x in enumerate(Zero_access_2) if x]
indx_Smoke_bot = [i for i, x in enumerate(Smoke_bot) if x]
if CTU_NERIS:
indx_CTU_neris = [i for i, x in enumerate(CTU_neris) if x]
# indx_SARUMAN1 = [i for i, x in enumerate(SARUMAN1) if x]
# indx_SARUMAN2 = [i for i, x in enumerate(SARUMAN2) if x]
# indx_SARUMAN3 = [i for i, x in enumerate(SARUMAN3) if x]
# indx_SARUMAN4 = [i for i, x in enumerate(SARUMAN4) if x]
# indx_SARUMAN5 = [i for i, x in enumerate(SARUMAN5) if x]
# indx_SARUMAN6 = [i for i, x in enumerate(SARUMAN6) if x]
# indx_SARUMAN7 = [i for i, x in enumerate(SARUMAN7) if x]
# indx_SARUMAN8 = [i for i, x in enumerate(SARUMAN8) if x]
# indx_SARUMAN9 = [i for i, x in enumerate(SARUMAN9) if x]
total_instances = data_df.shape[0]
if DEBUG:
print("Total Instances:" + str(total_instances))
print(
"bin_IRC_1_Instances:"
+ str(len(indx_IRC_1))
+ " ---> "
+ str(round(len(indx_IRC_1) / total_instances * 100, 6))
+ " %"
)
print(
"bin_IRC_2_Instances:"
+ str(len(indx_IRC_2))
+ " ---> "
+ str(round(len(indx_IRC_2) / total_instances * 100, 6))
+ " %"
)
print(
"bin_IRC_3_Instances:"
+ str(len(indx_IRC_3))
+ " ---> "
+ str(round(len(indx_IRC_3) / total_instances * 100, 6))
+ " %"
)
print(
"bin_IRC_4_Instances:"
+ str(len(indx_IRC_4))
+ " ---> "
+ str(round(len(indx_IRC_4) / total_instances * 100, 6))
+ " %"
)
print(
"bin_IRC_5_Instances:"
+ str(len(indx_IRC_5))
+ " ---> "
+ str(round(len(indx_IRC_5) / total_instances * 100, 6))
+ " %"
)
print(
"bin_IRC_6_Instances:"
+ str(len(indx_IRC_6))
+ " ---> "
+ str(round(len(indx_IRC_6) / total_instances * 100, 6))
+ " %"
)
print(
"bin_IRC_7_Instances:"
+ str(len(indx_IRC_7))
+ " ---> "
+ str(round(len(indx_IRC_7) / total_instances * 100, 6))
+ " %"
)
print(
"bin_IRC_8_Instances:"
+ str(len(indx_IRC_8))
+ " ---> "
+ str(round(len(indx_IRC_8) / total_instances * 100, 6))
+ " %"
)
print(
"bin_IRC_9_Instances:"
+ str(len(indx_IRC_9))
+ " ---> "
+ str(round(len(indx_IRC_9) / total_instances * 100, 6))
+ " %"
)
print(
"bin_IRC_10_Instances:"
+ str(len(indx_IRC_10))
+ " ---> "
+ str(round(len(indx_IRC_10) / total_instances * 100, 6))
+ " %"
)
print(
"bin_IRC_11_Instances:"
+ str(len(indx_IRC_11))
+ " ---> "
+ str(round(len(indx_IRC_11) / total_instances * 100, 6))
+ " %"
)
print(
"bin_IRC_12_Instances:"
+ str(len(indx_IRC_12))
+ " ---> "
+ str(round(len(indx_IRC_12) / total_instances * 100, 6))
+ " %"
)
print(
"bin_IRC_13_Instances:"
+ str(len(indx_IRC_13))
+ " ---> "
+ str(round(len(indx_IRC_13) / total_instances * 100, 6))
+ " %"
)
print(
"bin_IRC_14_Instances:"
+ str(len(indx_IRC_14))
+ " ---> "
+ str(round(len(indx_IRC_14) / total_instances * 100, 6))
+ " %"
)
print(
"bin_IRC_15_Instances:"
+ str(len(indx_IRC_15))
+ " ---> "
+ str(round(len(indx_IRC_15) / total_instances * 100, 6))
+ " %"
)
print(
"Neris_Instances:"
+ str(len(indx_Neris))
+ " ---> "
+ str(round(len(indx_Neris) / total_instances * 100, 6))
+ " %"
)
print(
"RBot_Instances:"
+ str(len(indx_RBot))
+ " ---> "
+ str(round(len(indx_RBot) / total_instances * 100, 6))
+ " %"
)
print(
"Menti_Instances:"
+ str(len(indx_Menti))
+ " ---> "
+ str(round(len(indx_Menti) / total_instances * 100, 6))
+ " %"
)
print(
"Sogou_Instances:"
+ str(len(indx_Sogou))
+ " ---> "
+ str(round(len(indx_Sogou) / total_instances * 100, 6))
+ " %"
)
print(
"Murlo_Instances:"
+ str(len(indx_Murlo))
+ " ---> "
+ str(round(len(indx_Murlo) / total_instances * 100, 6))
+ " %"
)
print(
"Virut_Instances:"
+ str(len(indx_Virut))
+ " ---> "
+ str(round(len(indx_Virut) / total_instances * 100, 6))
+ " %"
)
print(
"Black_hole_1_Instances:"
+ str(len(indx_Black_hole_1))
+ " ---> "
+ str(round(len(indx_Black_hole_1) / total_instances * 100, 6))
+ " %"
)
print(
"Black_hole_2_Instances:"
+ str(len(indx_Black_hole_2))
+ " ---> "
+ str(round(len(indx_Black_hole_2) / total_instances * 100, 6))
+ " %"
)
print(
"Black_hole_3_Instances:"
+ str(len(indx_Black_hole_3))
+ " ---> "
+ str(round(len(indx_Black_hole_3) / total_instances * 100, 6))
+ " %"
)
print(
"TBot_1_Instances:"
+ str(len(indx_TBot_1))
+ " ---> "
+ str(round(len(indx_TBot_1) / total_instances * 100, 6))
+ " %"
)
print(
"TBot_2_Instances:"
+ str(len(indx_TBot_2))
+ " ---> "
+ str(round(len(indx_TBot_2) / total_instances * 100, 6))
+ " %"
)
print(
"TBot_3_Instances:"
+ str(len(indx_TBot_3))
+ " ---> "
+ str(round(len(indx_TBot_3) / total_instances * 100, 6))
+ " %"
)
print(
"TBot_4_Instances:"
+ str(len(indx_TBot_4))
+ " ---> "
+ str(round(len(indx_TBot_4) / total_instances * 100, 6))
+ " %"
)
print(
"Weasel_master_Instances:"
+ str(len(indx_Weasel_master))
+ " ---> "
+ str(round(len(indx_Weasel_master) / total_instances * 100, 6))
+ " %"
)
print(
"Weasel_bot_Instances:"
+ str(len(indx_Weasel_bot))
+ " ---> "
+ str(round(len(indx_Weasel_bot) / total_instances * 100, 6))
+ " %"
)
print(
"Zeus_1_Instances:"
+ str(len(indx_Zeus_1))
+ " ---> "
+ str(round(len(indx_Zeus_1) / total_instances * 100, 6))
+ " %"
)
print(
"Zeus_2_Instances:"
+ str(len(indx_Zeus_2))
+ " ---> "
+ str(round(round(len(indx_Zeus_2) / total_instances * 100, 6), 2))
+ " %"
)
print(
"Zeus_3_Instances:"
+ str(len(indx_Zeus_3))
+ " ---> "
+ str(round(len(indx_Zeus_3) / total_instances * 100, 6))
+ " %"
)
print(
"bin_Zeus_Instances:"
+ str(len(indx_bin_Zeus))
+ " ---> "
+ str(round(len(indx_bin_Zeus) / total_instances * 100, 6))
+ " %"
)
print(
"Osx_trojan_Instances:"
+ str(len(indx_Osx_trojan))
+ " ---> "
+ str(round(len(indx_Osx_trojan) / total_instances * 100, 6))
+ " %"
)
print(
"Zero_access_1_Instances:"
+ str(len(indx_Zero_access_1))
+ " ---> "
+ str(round(len(indx_Zero_access_1) / total_instances * 100, 6))
+ " %"
)
print(
"Zero_access_2_Instances:"
+ str(len(indx_Zero_access_2))
+ " ---> "
+ str(round(len(indx_Zero_access_2) / total_instances * 100, 6))
+ " %"
)
print(
"Smoke_bot_Instances:"
+ str(len(indx_Smoke_bot))
+ " ---> "
+ str(round(len(indx_Smoke_bot) / total_instances * 100, 6))
+ " %"
)
if CTU_NERIS:
print(
"CTU_neris_instances:"
+ str(len(indx_CTU_neris))
+ " ---> "
+ str(round(len(indx_CTU_neris) / total_instances * 100, 6))
+ " %"
)
# print("indx_SARUMAN1_instances:" + str(len(indx_SARUMAN1)) + " ---> "+ str(round(len(indx_SARUMAN1)/total_instances*100, 6)) + " %")
# print("indx_SARUMAN2_instances:" + str(len(indx_SARUMAN2)) + " ---> "+ str(round(len(indx_SARUMAN2)/total_instances*100, 6)) + " %")
# print("indx_SARUMAN3_instances:" + str(len(indx_SARUMAN3)) + " ---> "+ str(round(len(indx_SARUMAN3)/total_instances*100, 6)) + " %")
# print("indx_SARUMAN4_instances:" + str(len(indx_SARUMAN4)) + " ---> "+ str(round(len(indx_SARUMAN4)/total_instances*100, 6)) + " %")
# print("indx_SARUMAN5_instances:" + str(len(indx_SARUMAN5)) + " ---> "+ str(round(len(indx_SARUMAN5)/total_instances*100, 6)) + " %")
# print("indx_SARUMAN6_instances:" + str(len(indx_SARUMAN6)) + " ---> "+ str(round(len(indx_SARUMAN6)/total_instances*100, 6)) + " %")
# print("indx_SARUMAN7_instances:" + str(len(indx_SARUMAN7)) + " ---> "+ str(round(len(indx_SARUMAN7)/total_instances*100, 6)) + " %")
# print("indx_SARUMAN8_instances:" + str(len(indx_SARUMAN8)) + " ---> "+ str(round(len(indx_SARUMAN8)/total_instances*100, 6)) + " %")
# print("indx_SARUMAN9_instances:" + str(len(indx_SARUMAN9)) + " ---> "+ str(round(len(indx_SARUMAN9)/total_instances*100, 6)) + " %")
# This cell labels the 'Label' column in the data frame to 1 where the particular botnet was found
if CAT_FEATURES == 1:
data.loc[:, "Label"] = "Normal"
data.loc[indx_IRC_1, "Label"] = "IRC_1"
data.loc[indx_IRC_2, "Label"] = "IRC_2"
data.loc[indx_IRC_3, "Label"] = "IRC_3"
data.loc[indx_IRC_4, "Label"] = "IRC_4"
data.loc[indx_IRC_5, "Label"] = "IRC_5"
data.loc[indx_IRC_6, "Label"] = "IRC_6"
data.loc[indx_IRC_7, "Label"] = "IRC_7"
data.loc[indx_IRC_11, "Label"] = "IRC_11"
data.loc[indx_IRC_15, "Label"] = "IRC_15"
data.loc[indx_Neris, "Label"] = "Neris"
data.loc[indx_RBot, "Label"] = "RBot"
data.loc[indx_Menti, "Label"] = "Menti"
data.loc[indx_Sogou, "Label"] = "Sogou"
data.loc[indx_Murlo, "Label"] = "Murlo"
data.loc[indx_Virut, "Label"] = "Virut"
data.loc[indx_Black_hole_1, "Label"] = "Black_hole_1"
data.loc[indx_Black_hole_2, "Label"] = "Black_hole_2"
data.loc[indx_Black_hole_3, "Label"] = "Black_hole_3"
data.loc[indx_TBot_1, "Label"] = "TBot_1"
data.loc[indx_TBot_2, "Label"] = "TBot_2"
data.loc[indx_TBot_3, "Label"] = "TBot_3"
data.loc[indx_TBot_4, "Label"] = "TBot_4"
data.loc[indx_Weasel_master, "Label"] = "Weasel_master"
data.loc[indx_Weasel_bot, "Label"] = "Weasel_bot"
data.loc[indx_Zeus_1, "Label"] = "Zeus_1"
data.loc[indx_Zeus_2, "Label"] = "Zeus_2"
data.loc[indx_Zeus_3, "Label"] = "Zeus_3"
data.loc[indx_bin_Zeus, "Label"] = "bin_Zeus"
data.loc[indx_Osx_trojan, "Label"] = "Osx_trojan"
data.loc[indx_Zero_access_1, "Label"] = "Zero_access_1"
data.loc[indx_Zero_access_2, "Label"] = "Zero_access_2"
data.loc[indx_Smoke_bot, "Label"] = "Smoke_bot"
if CTU_NERIS:
data.loc[indx_CTU_neris, "Label"] = "CTU_neris"
else:
data.loc[:, "Label"] = 1
data.loc[indx_IRC_1, "Label"] = 1
data.loc[indx_IRC_2, "Label"] = 2
data.loc[indx_IRC_3, "Label"] = 3
data.loc[indx_IRC_4, "Label"] = 4
data.loc[indx_IRC_5, "Label"] = 5
data.loc[indx_IRC_6, "Label"] = 6
data.loc[indx_IRC_7, "Label"] = 7
data.loc[indx_IRC_11, "Label"] = 8
data.loc[indx_IRC_15, "Label"] = 9
data.loc[indx_Neris, "Label"] = 10
data.loc[indx_RBot, "Label"] = 11
data.loc[indx_Menti, "Label"] = 12
data.loc[indx_Sogou, "Label"] = 13
data.loc[indx_Murlo, "Label"] = 14
data.loc[indx_Virut, "Label"] = 15
data.loc[indx_Black_hole_1, "Label"] = 16
data.loc[indx_Black_hole_2, "Label"] = 17
data.loc[indx_Black_hole_3, "Label"] = 18
data.loc[indx_TBot_1, "Label"] = 19
data.loc[indx_TBot_2, "Label"] = 20
data.loc[indx_TBot_3, "Label"] = 21
data.loc[indx_TBot_4, "Label"] = 22
data.loc[indx_Weasel_master, "Label"] = 23
data.loc[indx_Weasel_bot, "Label"] = 24
data.loc[indx_Zeus_1, "Label"] = 25
data.loc[indx_Zeus_2, "Label"] = 26
data.loc[indx_Zeus_3, "Label"] = 27
data.loc[indx_bin_Zeus, "Label"] = 28
data.loc[indx_Osx_trojan, "Label"] = 29
data.loc[indx_Zero_access_1, "Label"] = 30
data.loc[indx_Zero_access_2, "Label"] = 31
data.loc[indx_Smoke_bot, "Label"] = 32
if CTU_NERIS:
data.loc[indx_CTU_neris, "Label"] = 33
if USE_ONE_BOT:
one_bot_samples = data.loc[data["Label"] == 15].copy()
one_bot_samples.loc[:, "Label"] = 0
benign_samples = data.loc[data["Label"] == 1].copy()
data = pd.concat([benign_samples, one_bot_samples]).reset_index(
drop=True
) # Augmenting with real botnets
INPUT_FILE_NAME = INPUT_FILE_NAME + "_VIRUT"
elif USE_FOUR_BOTS:
# =============================================
# We will be using 4 Bots for ISCX Dataset
# =============================================
# ================== BOT 1 ==================
# bot_1_samples = data.loc[ data['Label']==15].copy() # Virut
# bot_1_samples.loc[:, 'Label'] = 1
benign_samples = data.loc[data["Label"] == 0].copy()
# data_frame = pd.concat([benign_samples, bot_1_samples]).reset_index(drop=True) #Augmenting with real botnets
# ================ ADD BOT 1 ================
bot_1_samples = data.loc[data["Label"] == 25].copy() # Zeus_1
bot_1_samples.loc[:, "Label"] = 1
data_frame = pd.concat([benign_samples, bot_1_samples]).reset_index(
drop=True
) # Augmenting with real botnets
# ================ ADD BOT 2 =================
bot_2_samples = data.loc[data["Label"] == 26].copy() # Zeus_2
bot_2_samples.loc[:, "Label"] = 1
data_frame = pd.concat([data_frame, bot_2_samples]).reset_index(
drop=True
) # Augmenting with real botnets
# ================ ADD BOT 3 =================
bot_3_samples = data.loc[data["Label"] == 27].copy() # bin_Zeus
bot_3_samples.loc[:, "Label"] = 1
data_frame = pd.concat([data_frame, bot_3_samples]).reset_index(
drop=True
) # Augmenting with real botnets
# ================ ADD BOT 4 =================
bot_4_samples = data.loc[data["Label"] == 28].copy() # Zeus_bin
bot_4_samples.loc[:, "Label"] = 1
data_frame = pd.concat([data_frame, bot_4_samples]).reset_index(
drop=True
) # Augmenting with real botnets
data = data_frame.copy()
INPUT_FILE_NAME = INPUT_FILE_NAME + "_4_BOTS"
elif USE_ALL_BOTS:
bot_samples = data.loc[data["Label"] != 0].copy()
bot_samples.loc[:, "Label"] = 1
benign_samples = data.loc[data["Label"] == 0].copy()
data = pd.concat([benign_samples, bot_samples]).reset_index(
drop=True
) # Augmenting with real botnets
# data.loc[indx_IRC_1, 'Label'] = 1
# data.loc[indx_IRC_2, 'Label'] = 1
# data.loc[indx_IRC_3, 'Label'] = 1
# data.loc[indx_IRC_4, 'Label'] = 1
# data.loc[indx_IRC_5, 'Label'] = 1
# data.loc[indx_IRC_6, 'Label'] = 1
# data.loc[indx_IRC_7, 'Label'] = 1
# data.loc[indx_IRC_11, 'Label'] = 1
# data.loc[indx_IRC_15, 'Label'] = 1
# data.loc[indx_Neris, 'Label'] = 1
# data.loc[indx_RBot, 'Label'] = 1
# data.loc[indx_Menti, 'Label'] = 1
# data.loc[indx_Sogou, 'Label'] = 1
# data.loc[indx_Murlo, 'Label'] = 1
# data.loc[indx_Virut, 'Label'] = 1
# data.loc[indx_Black_hole_1, 'Label'] = 1
# data.loc[indx_Black_hole_2, 'Label'] = 1
# data.loc[indx_Black_hole_3, 'Label'] = 1
# data.loc[indx_TBot_1, 'Label'] = 1
# data.loc[indx_TBot_2, 'Label'] = 1
# data.loc[indx_TBot_3, 'Label'] = 1
# data.loc[indx_TBot_4, 'Label'] = 1
# data.loc[indx_Weasel_master, 'Label'] = 1
# data.loc[indx_Weasel_bot, 'Label'] = 1
# data.loc[indx_Zeus_1, 'Label'] = 1
# data.loc[indx_Zeus_2, 'Label'] = 1
# data.loc[indx_Zeus_3, 'Label'] = 1
# data.loc[indx_bin_Zeus, 'Label'] = 1
# data.loc[indx_Osx_trojan, 'Label'] = 1
# data.loc[indx_Zero_access_1, 'Label'] = 1
# data.loc[indx_Zero_access_2, 'Label'] = 1
# data.loc[indx_Smoke_bot, 'Label'] = 1
# if CTU_NERIS:
# data.loc[indx_CTU_neris, 'Label'] = 1
# data.loc[indx_SARUMAN1, 'Label'] = 1
# data.loc[indx_SARUMAN2, 'Label'] = 1
# data.loc[indx_SARUMAN3, 'Label'] = 1
# data.loc[indx_SARUMAN4, 'Label'] = 1
# data.loc[indx_SARUMAN5, 'Label'] = 1
# data.loc[indx_SARUMAN6, 'Label'] = 1
# data.loc[indx_SARUMAN7, 'Label'] = 1
# data.loc[indx_SARUMAN8, 'Label'] = 1
# data.loc[indx_SARUMAN9, 'Label'] = 1
# =============================================================================================
# Print the dataset shape before preprocessing
# =============================================================================================
normal = data.loc[data["Label"] == 0].copy()
bots = data.loc[data["Label"] == 1].copy()
print("Before Preprocesing: Total: " + str(data.shape))
print("Before Preprocesing: Normal: " + str(normal.shape))
print("Before Preprocesing: Bots: " + str(bots.shape))
# =============================================================================================
# Check for any NULL values in the data & remove if any
# =============================================================================================
# replace inf with nan and then drop the rows with nans
if DEBUG:
print("Data Shape before droping NULL and INF values: ")
print(data.shape)
data = (
data.replace([np.inf, -np.inf], np.nan)
.dropna(how="any")
.reset_index(drop=True)
.copy()
)
# for cols in data.columns.tolist()[1:]:
if DEBUG:
print("Data Shape after droping NULL and INF values: ")
print(data.shape)
# =============================================================================================
data_df = data.drop(["Flow ID", "Label"], axis=1)
data_cols = data_df.columns
# print('')
# print(data_df.describe())
data_df = data_df[data[data_cols] >= 0].copy()
# print(data_df.describe())
# print('')
if DEBUG:
print("data_df after removing Label column")
print(data_df.shape)
# =============================================================================================
# Convert all values to float
# =============================================================================================
data_df = data_df.astype(float)
if DEBUG:
print(" Data Columns after converting to Float: " + str(data_df.columns))
# =============================================================================================
# Compute skew greater than 1 and less than -1 for suppressing outliers
# =============================================================================================
if REMOVE_SKEW:
skew = data_df.skew(axis=0, skipna=True)
print(skew)
high_skew_list = skew[skew > 20].index.tolist()
low_skew_list = skew[skew < 20].index.tolist()
if DEBUG:
print("low skew list" + str(low_skew_list))
print("high skew list" + str(high_skew_list))
# for i in high_skew_list:
# sns.distplot(data_df[i])
# print('$$$$$$$$$$$$$$$$$$$$$$$$$$$$$')
# plt.show()
data_df[high_skew_list] = np.log(data_df[high_skew_list].values + 1).copy()
data_df[low_skew_list] = np.log(data_df[low_skew_list].values + 1).copy()
# for i in high_skew_list:
# sns.distplot(data_df[i])
# print('$$$$$$$$$$$$$$$$$$$$$$$$$$$$$')
# plt.show()
# =============================================================================================
# Remove any left over inf values
# =============================================================================================
inf_indx = data_df.index[np.isinf(data_df).any(1)]
if DEBUG:
print("INF values before removing: " + str(inf_indx))
data_df = data_df.drop(inf_indx, axis=0).copy()
data = data.drop(inf_indx, axis=0).copy()
data_cols = data_df.columns
data[data_cols] = data_df.copy()
data = data.reset_index(drop=True).copy()
if DEBUG:
print("INF values removed and Data reindexed")
# =============================================================================================
# remove any columns with std = Zero
# =============================================================================================
if DEBUG:
print("Data before removing std = 0 columns")
print(data_df.shape)
print(data.shape)
data_df = data_df.loc[:, data_df.var() == 0]
data = data.drop(data_df.columns, axis=1)
if DEBUG:
print("Data after removing std = 0 columns")
print(data.shape)
# =============================================================================================
# Scale data between 0 and 1 for GAN input
# =============================================================================================
data_df = data.drop(["Flow ID", "Label"], axis=1)
print(data_df.describe())
data_df -= data_df.min()
data_df /= data_df.max()
print(
"HEREEEEEEEEEEEEEEEEEEEEEEEEEEE+++++++++++++++++++++++++++++++>>>>>>>>>>>>>>>>>>>>>>"
)
print(data_df.describe())
# =============================================================================================
# Check if there is any NaN
# =============================================================================================
inf_indx = data_df.isnull().sum().sum()
if DEBUG:
print("INF values: " + str(inf_indx))
inf_indx = data.index[np.isinf(data_df).any(1)]
if DEBUG:
print("Any Left over INF values: " + str(inf_indx))
data[data_df.columns] = data_df.copy()
# print(data.describe(include = 'all'))
# =============================================================================================
# Check count of bots in the dataset and save to the file
# =============================================================================================
# bots = data['Label'].value_counts()[1]
# total_flows = data.shape[0]
# print('Botnet counts are: '+ str(bots) + '(' + str(bots/total_flows * 100) + '%)\n' )
# =============================================================================================
data = data.drop(["Flow ID"], axis=1)
data = round(data, 8).copy()
if DEBUG:
print(" Data Columns after removing Flow ID: " + str(data.columns))
# =============================================================================================
# Check for any NULL values in the data & remove if any
# =============================================================================================
# replace inf with nan and then drop the rows with nans
if DEBUG:
print("Data Shape before droping NULL and INF values: ")
print(data.shape)
data = (
data.replace([np.inf, -np.inf], np.nan)
.dropna(how="any")
.reset_index(drop=True)
.copy()
)
if DEBUG:
print("Data Shape before droping NULL and INF values: ")
print(data.shape)
# selected_features = ['Idle Max', 'Idle Mean', 'Packet Length Min', 'FIN Flag Count', 'FWD Init Win Bytes', 'Bwd Packet Length Min', 'Flow IAT Min', 'Idle Min', 'Subflow Fwd Bytes', 'Fwd IAT Min', 'Fwd Packet Length Min', 'Packet Length Std', 'Fwd Packets/s', 'Bwd Packets/s', 'Label']
# data = data[selected_features].copy()
# print(data.describe())
# # =============================================================================================
# print('File: ' + str(INPUT_FILE_NAME)+ '_(Preprocessed).csv Saving ...')
# data.to_csv(str(PATH) + str(INPUT_FILE_NAME)+ '_(Preprocessed).csv')
# print('File: ' + str(INPUT_FILE_NAME)+ '_(Preprocessed).csv saved to directory')
# # =============================================================================================
data = data.drop(["Flow IAT Std", "Fwd IAT Std", "CWR Flag Count"], axis=1).copy()
if USE_FEATURE_REDUCTION:
selected_columns = top_features(data, "XGB")
print(selected_columns)
data_frame = data.drop(["Label"], axis=1)
data_frame = data[selected_columns].copy()
data_frame["Label"] = data["Label"].copy()
data = data_frame.copy()
print(data.shape)
normal = data.loc[data["Label"] == 0].copy()
bots = data.loc[data["Label"] == 1].copy()
print("After Preprocesing: Total: " + str(data.shape))
print("After Preprocesing: Normal: " + str(normal.shape))
print("After Preprocesing: Bots: " + str(bots.shape))
# =============================================================================================
print("File: " + str(INPUT_FILE_NAME) + "_(Preprocessed).csv Saving ...")
data.to_csv(str(PATH) + str(INPUT_FILE_NAME) + "_(Preprocessed).csv")
print("File: " + str(INPUT_FILE_NAME) + "_(Preprocessed).csv saved to directory")
# =============================================================================================