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post_scan_scripts_plot.py
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post_scan_scripts_plot.py
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"""
Post processing script
@author Peter Jose
"""
# TODO: Fix Bug in reading encoded data from the file
from cProfile import label
from calendar import day_abbr
from distutils.command.config import config
from fileinput import filename
import ipaddress
from os import minor
from re import A
from sqlite3 import Timestamp
import subprocess
from tkinter.tix import Tree
from turtle import width
import matplotlib
import pkg_resources
import argparse
import sys
import json
import pathlib
# import urllib.request
import time
from datetime import datetime, date
from urllib3 import Retry
############################# CONFIG START #############################
# Enable the flag to print the debug statments
ENABLE_DEBUG_PRINT_FLAG = True # True / False
# Flag to allow the installation of missing packages
ALLOW_INSTALLATION_OF_MISSING_PKGS_FLAG = True # True / False
# File name format
ZGRAB_OUT_FILE = "_zgrab2_out.json"
ZGRAB_META_FILE = "_scan_summary.json"
ZMAP_META_FILE = "_zmap_metadata.json"
AS_LINK= 'https://publicdata.caida.org/datasets/routing/routeviews6-prefix2as/'
AS_LOG_FILE='pfx2as-creation.log'
AS_DIR_NAME='additional_external_db'
ZGRAB_OUT_FILE_KEY = "zgrab2_out_file_path"
AS_FILE_PATH_KEY = "AS_file_path"
AS_COUNTRY_FILE_PATH_KEY = "AS_Country_file_path"
AS_CATEGORY_FILE_PATH_KEY = "AS_Category_file_path"
PROTOCOL_KEY = "protocol"
PORT_KEY = "port"
SCAN_CODE_KEY = "scan_code"
HITRATE_KEY = 'hitrate'
PROCESSED_DATA_DIR_NAME = "processed_data"
DATA_FRAME_FILENAME = "DataFrame.json"
MAX_MIND_DATABASE_USAGE_DATE = '11-06-2022'
MAX_MIND_GEOLITE2_DB_COMMON_PREFIX = 'GeoLite2-'
MAX_MIND_GEOLITE2_ASN_DB_NAME = 'GeoLite2-ASN'
MAX_MIND_GEOLITE2_COUNTRY_DB_NAME = 'GeoLite2-Country'
MAX_MIND_LICENSE_KEY = 'YOUR_LICENSE_KEY'
MAX_MIND_AS_DB_LINK = 'https://download.maxmind.com/app/geoip_download?edition_id=GeoLite2-ASN&license_key='+MAX_MIND_LICENSE_KEY+'&suffix=tar.gz'
MAX_MIND_COUNTRY_DB_LINK = 'https://download.maxmind.com/app/geoip_download?edition_id=GeoLite2-Country&license_key='+MAX_MIND_LICENSE_KEY+'&suffix=tar.gz'
STANFORD_ASDB_DATASET_LINK = "https://asdb.stanford.edu/data/"
STANFORD_ASDB_DATASET_FILE_NAME_SUFFIX = "_categorized_ases.csv"
############################## CONFIG END ##############################
# Check if the required modules for the script are installed
required = {'pysubnettree','pandas','matplotlib','seaborn','certifi','ipaddress','upsetplot','geopandas','pycountry','geoip2'}
installed = {pkg.key for pkg in pkg_resources.working_set}
missing = required - installed
if len(missing) != 0:
print("Following packages are missing : ", missing)
if ALLOW_INSTALLATION_OF_MISSING_PKGS_FLAG:
print ("Installing the missing packages")
python = sys.executable
subprocess.check_call([python, '-m', 'pip', 'install', *missing], stdout=subprocess.DEVNULL)
else:
exit()
import SubnetTree
import ipaddress
import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator
import pandas as pd
import numpy as np
import seaborn as sns
from upsetplot import from_contents, UpSet, plot
import geopandas
import pycountry
import geoip2.database
import filter_package
# import matplotlib.dates as mdates
plt.style.use('tableau-colorblind10')
colour_list = ['#FF5757', # red
'#145AE6', #blue
'#FFDF36', #yellow
# '#8C52FF', # purple
'#7ED957', #'#6EB84E',#green
'#7389ED',#'#5271FF', # royal blue
'#FF914D', #orange
'#727272', # grey
'#008037','#CB6CE6','#E9E9E9',
'#545454']
colour_alpha = 0.85
font_size_default = 10
# cycler(color, [#006BA4, #FF800E, #ABABAB, #595959,
# #5F9ED1, #C85200, #898989, #A2C8EC, #FFBC79, #CFCFCF])
def data_scraper(item):
"""
Function to extract the data from log files
Args:
item: item contrains the all the information regarding one protocol
Returns:
Nil
Raises:
Nil
"""
# print(item)
processed_data = []
zgrab2_outFile_name = item[ZGRAB_OUT_FILE_KEY]
AS_file_name = item[AS_FILE_PATH_KEY]
protocol = item[PROTOCOL_KEY]
port = item[PORT_KEY]
UseMaxMindDB_Flag = True
try:
if(datetime.strptime(item[SCAN_CODE_KEY], '%d-%m-%Y').date() < datetime.strptime(MAX_MIND_DATABASE_USAGE_DATE, '%d-%m-%Y').date()):
UseMaxMindDB_Flag = False
except Exception as e:
print("Error", repr(e))
if not UseMaxMindDB_Flag:
as_country_df = pd.DataFrame.from_dict(filter_package.create_AS_country_dict_fn(item[AS_COUNTRY_FILE_PATH_KEY]),orient='index').reset_index()
# subnet tree with AS info
tree = SubnetTree.SubnetTree()
if AS_file_name:
with open(AS_file_name, 'r') as AS_file:
try:
for line in AS_file:
AS_data = line.strip().split("\t")
try:
tree[AS_data[0]+"/"+AS_data[1]] = AS_data[2]
except ValueError as e:
print("Skipped line '" + line)
except Exception as e:
print("Error", repr(e))
else:
try:
ip_as_reader = geoip2.database.Reader(item[AS_FILE_PATH_KEY])
ip_as_country_reader = geoip2.database.Reader(item[AS_COUNTRY_FILE_PATH_KEY])
except Exception as e:
pass
print("Error", repr(e))
ip_list = []
try:
as_category_info_df = pd.read_csv(item[AS_CATEGORY_FILE_PATH_KEY])
as_category_info_df = as_category_info_df[as_category_info_df.columns[0:5]]
as_category_info_df.fillna('-', inplace=True)
as_category_info_df = as_category_info_df.assign(ASN = lambda x: (x['ASN'].replace('AS','',regex=True)))
except Exception as e:
pass
print("Error", repr(e))
with open(zgrab2_outFile_name,'r') as zgrab2_outFile:
for line in zgrab2_outFile:
zgrab_data = json.loads(line)
# check if the ip is already processed
if not zgrab_data["ip"] in ip_list:
ip_list.append(zgrab_data["ip"])
AS_info ='unknown'
tls,status,error,response, = "0",'-','-','-'
ipv6_64, country ='-','-'
clientTLS,serverTLS = '-','-'
certi_Issuer_commonName,certi_Issuer_country,certi_Issuer_org,validityStart,validityEnd = '-','-','-','-','-'
self_signed,browser_trusted,browser_error,scan_time,certificate_raw,fingerprint_sha256='-','-','-','-','-','-',
asn_category1L1,asn_category1L2,asn_category2L1,asn_category2L2='-','-','-','-'
signature_algorithm,length,server_cipher_suite='-','-','-'
try:
ipv6_64 = ipaddress.ip_address(zgrab_data["ip"].strip()).exploded.upper()[0:19]
except Exception as e:
# print("Error:",protocol, repr(e),line)
pass
if not UseMaxMindDB_Flag:
try:
AS_info = tree[zgrab_data["ip"]].replace(","," ")
except Exception as e:
# print("Error:",protocol, repr(e),line)
pass
try:
AS_list = AS_info.split(" ")
country_names = ""
for AS in AS_list:
country_list = as_country_df[as_country_df["asn"] ==AS]["country"].tolist()
for country_name in country_list:
country_names = country_names + str(country_name) + " "
if country_names != "":
country = country_name
except Exception as e:
print("Error: country",protocol, repr(e),line)
pass
else:
try:
AS_info = str(ip_as_reader.asn(zgrab_data["ip"]).autonomous_system_number)
except Exception as e:
print("AS info:",protocol, repr(e),line)
pass
try:
country = ip_as_country_reader.country(zgrab_data["ip"]).country.iso_code
except Exception as e:
print("AS info:",protocol, repr(e),line)
pass
try:
row = as_category_info_df[as_category_info_df['ASN'] == AS_info]
if not row.empty:
asn_category1L1 = row.iloc[0]['Category 1 - Layer 1']
asn_category1L2 = row.iloc[0]['Category 1 - Layer 2']
asn_category2L1 = row.iloc[0]['Category 2 - Layer 1']
asn_category2L2 = row.iloc[0]['Category 2 - Layer 2']
except Exception as e:
print(row)
print("AS category:",protocol, repr(e),line)
pass
try:
status = zgrab_data["data"][protocol]["status"]
if not zgrab_data["data"][protocol]["status"] == "success":
error = zgrab_data["data"][protocol]["error"]
if zgrab_data["data"][protocol]["status"] == "protocol-error":
try:
pass
response = zgrab_data["data"][protocol]["result"][protocol].split(' ')[0]
except Exception as e:
response = 'can\'t parse'
# print("Error:", protocol, repr(e),line)
pass
else:
try:
for key_ in zgrab_data["data"][protocol]["result"]:
if key_ == protocol:
response = zgrab_data["data"][protocol]["result"][protocol].strip('\n')
elif key_ == "tls":
tls = "1"
tls_pointer = zgrab_data["data"][protocol]["result"]["tls"]
clientTLS = tls_pointer["handshake_log"]["client_hello"]["version"]["name"]
if "supported_versions" in tls_pointer["handshake_log"]["server_hello"].keys():
serverTLS = tls_pointer["handshake_log"]["server_hello"]["supported_versions"]["selected_version"]["name"]
else :
serverTLS = tls_pointer["handshake_log"]["server_hello"]["version"]["name"]
if "cipher_suite" in tls_pointer["handshake_log"]["server_hello"].keys():
server_cipher_suite = tls_pointer["handshake_log"]["server_hello"]["cipher_suite"]["name"]
certificate_raw = tls_pointer["handshake_log"]["server_certificates"]["certificate"]["raw"]
fingerprint_sha256 = tls_pointer["handshake_log"]["server_certificates"]["certificate"]["parsed"]["subject_key_info"]["fingerprint_sha256"]
if "rsa_public_key" in tls_pointer["handshake_log"]["server_certificates"]["certificate"]["parsed"]["subject_key_info"].keys():
length = tls_pointer["handshake_log"]["server_certificates"]["certificate"]["parsed"]["subject_key_info"]["rsa_public_key"]["length"]
elif "ecdsa_public_key" in tls_pointer["handshake_log"]["server_certificates"]["certificate"]["parsed"]["subject_key_info"].keys():
length = tls_pointer["handshake_log"]["server_certificates"]["certificate"]["parsed"]["subject_key_info"]["ecdsa_public_key"]["length"]
if "self_signed" in tls_pointer["handshake_log"]["server_certificates"]["certificate"]["parsed"]["signature"].keys():
if tls_pointer["handshake_log"]["server_certificates"]["certificate"]["parsed"]["signature"]["self_signed"] == True:
self_signed = 'Self-signed'
else:
self_signed = 'Not self-signed'
if "common_name" in tls_pointer["handshake_log"]["server_certificates"]["certificate"]["parsed"]["issuer"].keys():
certi_Issuer_commonName = tls_pointer["handshake_log"]["server_certificates"]["certificate"]["parsed"]["issuer"]["common_name"]
if "country" in tls_pointer["handshake_log"]["server_certificates"]["certificate"]["parsed"]["issuer"].keys():
certi_Issuer_country = tls_pointer["handshake_log"]["server_certificates"]["certificate"]["parsed"]["issuer"]["country"]
if "organization" in tls_pointer["handshake_log"]["server_certificates"]["certificate"]["parsed"]["issuer"].keys():
certi_Issuer_org = tls_pointer["handshake_log"]["server_certificates"]["certificate"]["parsed"]["issuer"]["organization"]
if "signature_algorithm" in tls_pointer["handshake_log"]["server_certificates"]["certificate"]["parsed"].keys():
signature_algorithm = tls_pointer["handshake_log"]["server_certificates"]["certificate"]["parsed"]["signature_algorithm"]["name"]
validityStart = tls_pointer["handshake_log"]["server_certificates"]["certificate"]["parsed"]["validity"]["start"]
validityEnd = tls_pointer["handshake_log"]["server_certificates"]["certificate"]["parsed"]["validity"]["end"]
if tls_pointer["handshake_log"]["server_certificates"]["validation"]["browser_trusted"] == True:
browser_trusted = 'Browser Trusted'
else:
browser_trusted = 'Browser Not Trusted'
if "browser_error" in tls_pointer["handshake_log"]["server_certificates"]["validation"].keys():
browser_error = tls_pointer["handshake_log"]["server_certificates"]["validation"]["browser_error"]
scan_time = zgrab_data["data"][protocol]["timestamp"]
except Exception as e:
print("Error 3",protocol, repr(e),line)
pass
outputDict = {"ip":zgrab_data["ip"],"port":port,"AS":AS_info,"protocol":protocol,"ipv6_64":ipv6_64,"country":country,
"status":status,"error":error,"response":response,"tls":tls,"clientTLS":clientTLS,
"serverTLS":serverTLS,"certificate_raw":certificate_raw, "fingerprint_sha256":fingerprint_sha256,"length":length,"server_cipher_suite":server_cipher_suite,
"certi_Issuer_commonName":certi_Issuer_commonName,"certi_Issuer_country":certi_Issuer_country,
"certi_Issuer_org":certi_Issuer_org,"self_signed":self_signed,"signature_algorithm":signature_algorithm,
"validityStart":validityStart,"validityEnd":validityEnd, "browser_trusted":browser_trusted,
"browser_error":browser_error,"scan_time":scan_time,"scan_code":item[SCAN_CODE_KEY],
"asn_category1L1":asn_category1L1,"asn_category1L2":asn_category1L2,"asn_category2L1":asn_category2L1,
"asn_category2L2":asn_category2L2}
processed_data.append(outputDict)
except Exception as err:
print("Error:",protocol, err,line)
if UseMaxMindDB_Flag:
ip_as_reader.close()
ip_as_country_reader.close()
return processed_data
def create_process_list(scan_path,categorized_as_file_name):
"""Function to create the list of files to be used for further processing
Args:
scan_path: scan path input to the program
categorized_as_file_name: file name of DB for categorizing AS
Returns:
Nil
Raises:
Nil
"""
process_list= []
# create AS directory
AS_path = pathlib.Path(scan_path,AS_DIR_NAME)
if not pathlib.Path.is_dir(AS_path):
pathlib.Path.mkdir(AS_path)
# download the AS log file
# try:
# with urllib.request.urlopen(AS_LINK +AS_LOG_FILE, cafile=certifi.where()) as response:
# html = response.read()
# AS_log_file_list = bytes.decode(html).split('\n')
# except Exception as e:
# print("Error", repr(e))
# AS_log_file_list = []
AS_log_file_list = []
# get all the zgrab output files
scan_output_list = ([x.name for x in scan_path.iterdir() if x.is_dir()])
if "coap" in scan_output_list:
pass
# scan_output_list.remove("coap")
for folder in scan_output_list:
zgrab2_output_files_list = ([x for x in pathlib.Path(scan_path,folder).iterdir() if (x.is_file() and x.name.find(ZGRAB_META_FILE) != -1)])
for zgrab2_summary_file in zgrab2_output_files_list:
fileName = zgrab2_summary_file.name
fileDetails = fileName.split('_')
protocol = fileDetails[0]
port = fileDetails[2]
zgrab_summary = open(zgrab2_summary_file,'r').read()
zgrab2_out_file = str(zgrab2_summary_file).replace(ZGRAB_META_FILE,ZGRAB_OUT_FILE)
scanDate = date.today()
AS_file_path_final = ''
AS_country_file_path = ''
AS_Category_file_path = ''
##### AS and AS Country
try:
scanDate = datetime.strptime(scan_path.name, '%d-%m-%Y').date()
except Exception as e:
print("Error", repr(e))
if(scanDate < datetime.strptime(MAX_MIND_DATABASE_USAGE_DATE, '%d-%m-%Y').date()):
try:
date_ = ((json.loads(zgrab_summary))["start"].split('T')[0]).replace('-','')
# Get AS files
AS_already_downloaded_files_list = ([x for x in AS_path.iterdir() if (x.is_file() and x.name.find(date_) != -1 and x.suffix != '.gz')])
if not len(AS_already_downloaded_files_list):
for AS_log_file in AS_log_file_list:
if AS_log_file.find(date_) != -1:
AS_file_relative_path=AS_log_file.split("\t")[-1]
AS_file_path=AS_file_relative_path.split('/')[-1]
AS_file_gz = pathlib.Path(AS_path,AS_file_path)
subprocess.call(["wget "+AS_LINK+AS_file_relative_path+" -P "+str(AS_path) +" --no-check-certificate && gunzip "+ str(AS_file_gz)],shell=True, timeout=60)
# urllib.request.urlretrieve(AS_LINK+AS_file_relative_path,AS_file_gz)
# with gzip.open(AS_file_gz, 'rb') as f_in:
# AS_file_path_final = pathlib.Path(AS_path,AS_file_path.replace('.gz',''))
# with open(AS_file_path_final, 'wb') as f_out:
# shutil.copyfileobj(f_in, f_out)
# AS_file_path_final = str(AS_file_path_final)
break
else:
AS_file_path_final = str(AS_already_downloaded_files_list[0])
AS_already_downloaded_files_list = ([x for x in AS_path.iterdir() if (x.is_file() and x.name.find('as-org2info') != -1 and x.suffix != '.gz')])
if len(AS_already_downloaded_files_list):
AS_country_file_path = str(AS_already_downloaded_files_list[0].resolve())
except Exception as e:
print("Error", repr(e))
else:
try:
AS_already_downloaded_files_list = ([x for x in AS_path.iterdir() if (x.is_dir() and x.name.find(MAX_MIND_GEOLITE2_DB_COMMON_PREFIX) != -1)])
if not len(AS_already_downloaded_files_list):
subprocess.call(["cd "+str(AS_path.resolve())+";"+
"wget \""+MAX_MIND_AS_DB_LINK+"\" -O "+MAX_MIND_GEOLITE2_ASN_DB_NAME+".tar.gz --no-check-certificate && tar -xvzf "+
MAX_MIND_GEOLITE2_ASN_DB_NAME+".tar.gz"],shell=True, timeout=60)
subprocess.call(["cd "+str(AS_path.resolve())+";"+
"wget \""+MAX_MIND_COUNTRY_DB_LINK+"\" -O "+MAX_MIND_GEOLITE2_COUNTRY_DB_NAME+".tar.gz --no-check-certificate && tar -xvzf "+
MAX_MIND_GEOLITE2_COUNTRY_DB_NAME+".tar.gz"],shell=True, timeout=60)
AS_already_downloaded_files_list = ([x for x in AS_path.iterdir() if (x.is_dir() and x.name.find(MAX_MIND_GEOLITE2_DB_COMMON_PREFIX) != -1)])
for GeoLite_file in AS_already_downloaded_files_list:
if GeoLite_file.name.find(MAX_MIND_GEOLITE2_ASN_DB_NAME) != -1:
AS_file_path_final = str(pathlib.Path(GeoLite_file,MAX_MIND_GEOLITE2_ASN_DB_NAME+'.mmdb').resolve())
elif GeoLite_file.name.find(MAX_MIND_GEOLITE2_COUNTRY_DB_NAME) != -1:
AS_country_file_path = str(pathlib.Path(GeoLite_file,MAX_MIND_GEOLITE2_COUNTRY_DB_NAME+'.mmdb').resolve())
except Exception as e:
print("Error", repr(e))
##### AS Category
try:
categorized_as_files_list = ([x for x in AS_path.iterdir() if (x.is_file() and x.name.find(STANFORD_ASDB_DATASET_FILE_NAME_SUFFIX) != -1 and x.suffix == '.csv')])
if not len(categorized_as_files_list):
if len(categorized_as_file_name):
subprocess.call(["cd "+str(AS_path.resolve())+";"+
"wget "+STANFORD_ASDB_DATASET_LINK+categorized_as_file_name+" --no-check-certificate"],shell=True, timeout=180)
categorized_as_files_list = ([x for x in AS_path.iterdir() if (x.is_file() and x.name.find(STANFORD_ASDB_DATASET_FILE_NAME_SUFFIX) != -1 and x.suffix == '.csv')])
AS_Category_file_path = str(categorized_as_files_list[0].resolve())
except Exception as e:
print("Error", repr(e))
process_list.append({ZGRAB_OUT_FILE_KEY:zgrab2_out_file,AS_FILE_PATH_KEY:AS_file_path_final,
PROTOCOL_KEY:protocol,PORT_KEY:port,SCAN_CODE_KEY:scan_path.name,AS_COUNTRY_FILE_PATH_KEY:AS_country_file_path,
AS_CATEGORY_FILE_PATH_KEY:AS_Category_file_path})
return process_list
def plot_hitrate(scan_path,
processed_data_dir,
file_name_prefix,
legendPosition_outside = True,
figsize = [10,10],
display_values = True):
zmap_data = []
try:
print("plot_hitrate")
# get all the zmap output files
scan_output_list = ([x.name for x in scan_path.iterdir() if x.is_dir()])
for folder in scan_output_list:
zmap_meta_files_list = ([x for x in pathlib.Path(scan_path,folder).iterdir() if (x.is_file() and x.name.find(ZMAP_META_FILE) != -1)])
for zmap_meta_file in zmap_meta_files_list:
fileName = zmap_meta_file.name
zmap_meta_details = open(zmap_meta_file,'r').read()
hitrate = 0
try:
fileContent = json.loads(zmap_meta_details)
port = fileContent['target_port']
hitrate = fileContent['hitrate']
except Exception as e:
print("Error",fileName, repr(e))
zmap_data.append({HITRATE_KEY:hitrate,PORT_KEY:port})
df = pd.DataFrame(zmap_data)
plt.rcParams["figure.figsize"] = figsize
fig, ax = plt.subplots(1, 1)
ax = df.plot.bar(x=PORT_KEY, y=HITRATE_KEY, rot=0,alpha=colour_alpha,log=True, width= 0.8)
ax.set_axisbelow(True)
ax.yaxis.grid(True, color='#EEEEEE')
ax.xaxis.grid(False)
if legendPosition_outside:
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5),prop={'size': font_size_default})
else:
ax.legend(prop={'size': font_size_default})
ax.set_title("Open Port Hitrate", pad=15, fontsize = font_size_default+2)
ax.set_xlabel("Open Port", labelpad=15, fontsize = font_size_default+2)
plt.xticks(rotation= 45,fontsize= font_size_default)
ax.set_ylabel("No. of IP Addresses(%)", labelpad=15, fontsize = font_size_default+2)
# if display_values:
# display_values_on_graph(ax,font_size=font_size_default,print_threshold=0,location='mid', round_position=4)
plt.savefig(str(pathlib.Path(processed_data_dir,file_name_prefix+'HitRate.png')), bbox_inches = 'tight', pad_inches = 0.1)
except Exception as e:
print("Error", repr(e))
###
# Display the value on graph
###
def display_values_on_graph(
ax,
float_flag = True,
keep_zero_value=False,
print_threshold=0,
font_size = 12,
location='top',
round_position = 1,
rotation = 0,
total_value_list = [],
display_content_colour = 'black'):
for bar in ax.patches:
# The text annotation for each bar should be its height.
bar_value = bar.get_height()
if keep_zero_value or bar_value > print_threshold:
# Format the text with commas to separate thousands. You can do
# any type of formatting here though.
if float_flag:
text = f'{round(bar_value,round_position):,}'
else:
text = f'{int(bar_value):,}'
# This will give the middle of each bar on the x-axis.
text_x = bar.get_x() + bar.get_width()/ 2
# get_y() is where the bar starts so we add the height to it.
if location == 'top':
text_y = bar.get_y() + bar_value
if rotation:
text_y = text_y + 150
# If we want the text to be the same color as the bar, we can
# get the color like so:
bar_color = 'black'#bar.get_facecolor()
# bar_color = 'black'
# If you want a consistent color, you can just set it as a constant, e.g. #222222
ax.text(text_x, text_y, text, ha='center', va='bottom', color=bar_color, alpha = 1,
size=font_size,rotation=rotation)
else:
text_y = bar.get_y() + bar_value / 2
# If we want the text to be the same color as the bar, we can
# get the color like so:
bar_color = display_content_colour
# If you want a consistent color, you can just set it as a constant, e.g. #222222
ax.text(text_x, text_y, text, ha='center', va='center', color=bar_color, alpha = 1,
size=font_size)
if len(total_value_list):
for bar,val in zip(ax.patches,total_value_list):
bar_value = 105
text_x = bar.get_x() + bar.get_width()/ 2
text_y = bar.get_y() + bar_value
bar_color = 'black'
ax.text(text_x, text_y, str(val), ha='center', va='center', color=bar_color, alpha = 1,rotation=90,
size=font_size)
####
# Function to plot the stacked Bar Plot
####
def plot_stackBarPlot(
plotData,
figName,
title = "",
labels_x = "",
labels_y = "",
legend_title = "",
legend_display=True,
legendPosition_outside = False,
plot_font_size = 0,
stacked_var = True,
display_values = False,
display_values_Float = True,
display_values_size = 0,
display_location='mid',
display_threshold=1,
figsize = [],#[12,10],
yticks_in_Kilo=False,
color_list=[],
rotate_value = 0,
total_value_list = [],
legend_loc = 'center left',
bbox_position = (1,0.5),
ncol = 1,
bar_width = 0.8,
total_y_label="Total"
):
if len(figsize):
plt.rcParams["figure.figsize"] = figsize
# plt.rcParams["boxplot.boxprops.linewidth"] = 0.8
# plt.rcParams["figure.autolayout"] = True
if plot_font_size == 0:
plot_font_size = font_size_default
fig, ax_ = plt.subplots(1, 1)
ax = ax_
if len(total_value_list):
fig, ax_ = plt.subplots(2, 1,sharex=True,gridspec_kw={'height_ratios': [1, 100]})
ax = ax_[1]
fig.tight_layout(h_pad=0.5)
if not len(color_list):
plotData.plot(kind='bar', fontsize=(plot_font_size-2),
stacked=stacked_var, alpha=colour_alpha,
width = bar_width,edgecolor = 'none', ax = ax,linewidth = 0.2)
else:
plotData.plot(kind='bar', fontsize=(plot_font_size-2),
stacked=stacked_var, alpha=colour_alpha,
width = bar_width,edgecolor = 'none',color=color_list, ax =ax,linewidth = 0.2)
# ax.grid(which='major', color='#DDDDDD', linewidth=0.8)
# ax.grid(which='minor', color='#EEEEEE', linestyle=':', linewidth=0.5)
ax.minorticks_off()
# ax.set_axisbelow(True)
xlabel_pad = 5
if not stacked_var:
# for i in range(len(ax.get_xticklabels())):
label_list = ax.get_xticklabels()
ax.set_xticks(np.arange(-0.5,len(ax.get_xticklabels()),1))
# ax.set_xticklabels([])
# print(label_list)
yticks_space = ax.get_yticks()[-1]/len(ax.get_yticks())
for lab in label_list:
ax.text(lab.get_position()[0],-1.5*yticks_space/10,lab.get_text(), ha='center', va='top', color='black', alpha = 1,rotation=90,
size=plot_font_size-2)
if len(lab.get_text())*3.5 > xlabel_pad:
xlabel_pad = len(lab.get_text())* 3.5
ax.set_xticklabels([])
ax.set_axisbelow(True)
ax.yaxis.grid(True, color='#EEEEEE')
ax.xaxis.grid(False)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
if legendPosition_outside:
ax.legend(title = legend_title,loc=legend_loc, bbox_to_anchor=bbox_position,title_fontsize=plot_font_size -1,fontsize = plot_font_size-2,ncol=ncol,frameon=False)
else:
ax.legend(title = legend_title,title_fontsize=plot_font_size-1,fontsize = plot_font_size-2,frameon=False)
if title != "":
ax.set_title(title, pad=15, fontsize = plot_font_size)
ax.set_xlabel(labels_x, labelpad=xlabel_pad, fontsize = plot_font_size-1)
ax.tick_params(axis = 'both', which = 'major', labelsize = plot_font_size - 2)
# plt.xticks(rotation= 45,fontsize= plot_font_size)
ax.set_ylabel(labels_y, labelpad=5, fontsize = plot_font_size-1)
if yticks_in_Kilo:
yticks = ['{:,.0f}'.format(x) + 'K' for x in ax.get_yticks()/1000]
ax.set_yticklabels(yticks)
if display_values:
if display_values_size == 0:
display_values_size = plot_font_size-3
display_values_on_graph(ax,font_size=display_values_size,
print_threshold=display_threshold,location=display_location,float_flag= display_values_Float, rotation= rotate_value)
if not legend_display:
ax.get_legend().remove()
if len(total_value_list):
if stacked_var:
incr = 0
else:
incr = int((len(ax.patches)/len(total_value_list))/2)
for i in range(len(total_value_list)):
val = total_value_list[i]
bar = ax.patches[i+incr]
text_x = bar.get_x()
if stacked_var:
text_x = text_x + bar.get_width()/2
else:
text_x = text_x + (bar.get_width()/2 if ((len(ax.patches)/len(total_value_list))%2 == 1) else bar.get_width())
bar_color = 'black'
ax_[0].text(text_x, 0, str(val), ha='center', va='center', color=bar_color, alpha = 1,
size=plot_font_size - 2)
ax_[0].set_ylabel(total_y_label, labelpad=17, fontsize = plot_font_size-1, rotation = 90)
ax_[0].spines['top'].set_visible(False)
ax_[0].spines['right'].set_visible(False)
ax_[0].spines['bottom'].set_visible(False)
ax_[0].set_yticks([])
ax_[0].tick_params(bottom=False)
ax_[0].xaxis.set_ticks_position('none')
plt.savefig(figName, bbox_inches = 'tight', pad_inches = 0.1,dpi = 600)
####
# Function to plot scan summary
####
def plot_scan_summary(
dataFrame,
processed_data_dir,
file_name_prefix):
try:
label_order = dataFrame['status'].unique().tolist()
label_order.remove("success")
label_order = ['success'] + label_order
plotData = dataFrame.groupby(['status','Protocol, Port'],sort=False)['status'].count().unstack('status')[label_order].fillna(0)
plot_stackBarPlot(plotData,str(pathlib.Path(processed_data_dir,file_name_prefix+'summary_raw.png')),
"","Protocol, Port","Count of IPs",legend_title="Scan output status",
legendPosition_outside= True, yticks_in_Kilo=True)
plotData = plotData.transform(lambda x: (x/x.sum())*100, axis=1)
plot_stackBarPlot(plotData,str(pathlib.Path(processed_data_dir,file_name_prefix+'summary_percentage.png')),
"","Protocol, Port","Percentage", legend_title="Scan output status",
legendPosition_outside= True,display_values=True)
except Exception as e:
print("Error", repr(e))
####
# Function to plot scan summary
####
def plot_scan_summary_with_total(
dataFrame,
processed_data_dir,
file_name_prefix):
try:
label_order = dataFrame['status'].unique().tolist()
label_order.remove("success")
label_order = ['success'] + label_order
total_value_list = dataFrame.groupby(['Protocol, Port'],sort=False)['status'].count().to_list()
plotData = dataFrame.groupby(['status','Protocol, Port'],sort=False)['status'].count().unstack('status')[label_order].fillna(0)
print(file_name_prefix)
print(plotData)
plotData = plotData.transform(lambda x: (x/x.sum())*100, axis=1)
print(plotData)
plot_stackBarPlot(plotData,str(pathlib.Path(processed_data_dir,file_name_prefix+'summary_percentage_with_total.pdf')),
"","Protocol, Port","No. of IP Addresses (%)", legend_title="Scan output status",
legendPosition_outside= True,display_values=True,total_value_list=total_value_list,figsize=[4.4,4], display_values_size=5,plot_font_size=8,
total_y_label="Total")
except Exception as e:
print("Error", repr(e))
def alpha3code(column):
CODE=[]
for country_code2 in column:
try:
code=pycountry.countries.get(alpha_2=country_code2).alpha_3
CODE.append(code)
except:
CODE.append('None')
return CODE
def ip_percentage_country_info(row,data):
alpha3 = row['iso_a3']
val = data[data['iso_a3'] == alpha3]
if val.empty:
return 0
return val['ip'].tolist()[0]
####
# Function to plot scan summary
####
def plot_scan_country_info(
dataFrame,
processed_data_dir,
file_name_prefix):
try:
plt.rcParams["figure.figsize"] = [5.5,4]
dataFrame = dataFrame[(dataFrame['status'] == 'success') & (dataFrame['country'] != '-') & (dataFrame['country'] != 'RU')]
dataFrame = dataFrame[(dataFrame['country'] != '-') & (dataFrame['country'] != 'RU')]
plotData = dataFrame.groupby(['country'],sort=False).count().fillna(0).reset_index()[['country','ip']]
plotData['ip'] = plotData['ip']/float(plotData['ip'].sum())*100
plotData = plotData.sort_values(by = ['ip'],ascending=False)
plotData['iso_a3'] = alpha3code(plotData.country)
fig, ax = plt.subplots(1, 1)
world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres'))
world['IP spread (%)'] = world.apply(ip_percentage_country_info,data = plotData, axis=1)
world = world[(world.name!="Antarctica")]
world.plot(column='IP spread (%)', ax=ax, linewidth=0.01, legend=True,edgecolor='black',legend_kwds={
'orientation': "horizontal",'shrink':0.4, 'pad':0.025},cmap='OrRd')
fig = ax.figure
cb_ax = fig.axes[1]
cb_ax.tick_params(labelsize=6)
cb_ax.set_xlabel('No. of IP Addresses (%)',fontsize=7)
ax.set_yticks([])
ax.set_xticks([])
ax.set_axis_off()
plt.rc('legend', fontsize=7)
plt.savefig(str(pathlib.Path(processed_data_dir,file_name_prefix+'country_wise_distribution.pdf')), bbox_inches = 'tight', pad_inches = 0.1,dpi = 600)
except Exception as e:
print("Error", repr(e))
##
# function to get the location wise statistics based on protocol
#
def get_location_statistics(
dataFrame):
protocol_port_list = dataFrame.sort_values(by = ['Protocol, Port'],ascending=True)['Protocol, Port'].unique().tolist()
dataFrame = dataFrame[dataFrame['status'] == 'success']
# for protocol_port in protocol_port_list:
# print(protocol_port)
# plotData = dataFrame[dataFrame['Protocol, Port'] == protocol_port].groupby(['country'],sort=True).count().fillna(0).reset_index()[['country','ip']]
# plotData = plotData.sort_values(by = ['ip'],ascending=False)
# plotData1 = dataFrame[dataFrame['Protocol, Port'] == protocol_port].groupby(['AS'],sort=True).count().fillna(0).reset_index()[['AS','ip']]
# plotData1 = plotData1.sort_values(by = ['ip'],ascending=False)
# print(plotData.head(n=5))
# print(plotData1.head(n=5))
print('xmpp')
ip1 = set(dataFrame[(dataFrame['port'].astype(str) == '5222')]['ip'].unique().tolist())
ip2 = set(dataFrame[(dataFrame['port'].astype(str) == '5223')]['ip'].unique().tolist())
print(len(ip1.intersection(ip2)))
print('amqp')
ip1 = set(dataFrame[(dataFrame['port'].astype(str) == '5671')]['ip'].unique().tolist())
ip2 = set(dataFrame[(dataFrame['port'].astype(str) == '5672')]['ip'].unique().tolist())
print(len(ip1.intersection(ip2)))
print('coap')
ip1 = set(dataFrame[(dataFrame['port'].astype(str) == '5684')]['ip'].unique().tolist())
ip2 = set(dataFrame[(dataFrame['port'].astype(str) == '5683')]['ip'].unique().tolist())
print(len(ip1.intersection(ip2)))
print('opc ua')
ip1 = set(dataFrame[(dataFrame['port'].astype(str) == '4840')]['ip'].unique().tolist())
ip2 = set(dataFrame[(dataFrame['port'].astype(str) == '4843')]['ip'].unique().tolist())
print(len(ip1.intersection(ip2)))
print('mqtt')
ip1 = set(dataFrame[(dataFrame['port'].astype(str) == '1883')]['ip'].unique().tolist())
ip2 = set(dataFrame[(dataFrame['port'].astype(str) == '8883')]['ip'].unique().tolist())
print(len(ip1.intersection(ip2)))
####
# Function to get location inferences
####
def scan_location_inferences(
dataFrame,
file_name_prefix):
try:
pass
# # To print the AS based distribution Total
# dataFrame = dataFrame[dataFrame['status'] == 'success']
# plotData = dataFrame.groupby(['country','AS'],sort=False)['country'].count().unstack('country').reset_index().fillna(0)
# plotData.to_csv(str(processed_data_dir.resolve())+"/"+"Country_AS_total.csv", sep='\t', index=False)
# print(plotData.head(n=10))
# print(plotData.shape)
# # To find Discovery Percentage
# print('US')
# plotData = dataFrame[dataFrame['country'] == 'US']
# plotData = plotData.groupby(['status'],sort=False).count().fillna(0).reset_index()[['status','ip']]
# # plotData['ip'] = plotData['ip']/float(plotData['ip'].sum())*100
# plotData = plotData.sort_values(by = ['ip'],ascending=False)
# print(plotData.head(n=10))
# print('DE')
# plotData = dataFrame[dataFrame['country'] == 'DE']
# plotData = plotData.groupby(['status'],sort=False).count().fillna(0).reset_index()[['status','ip']]
# # plotData['ip'] = plotData['ip']/float(plotData['ip'].sum())*100
# plotData = plotData.sort_values(by = ['ip'],ascending=False)
# print(plotData.head(n=10))
# print('CN')
# plotData = dataFrame[dataFrame['country'] == 'CN']
# plotData = plotData.groupby(['status'],sort=False).count().fillna(0).reset_index()[['status','ip']]
# # plotData['ip'] = plotData['ip']/float(plotData['ip'].sum())*100
# plotData = plotData.sort_values(by = ['ip'],ascending=False)
# print(plotData.head(n=10))
# dataFrame = dataFrame[(dataFrame['status'] == 'success')]
# plotData = dataFrame.groupby(['country'],sort=False).count().fillna(0).reset_index()[['country','ip']]
# plotData = plotData.sort_values(by = ['ip'],ascending=False)
# # plotData['ip'] = plotData['ip']/float(plotData['ip'].sum())*100
# print(plotData.head(n=10))
# # # To find Discovery Percentage
# plotData = dataFrame.groupby(['country'],sort=False).count().fillna(0).reset_index()[['country','ip']]
# # plotData['ip'] = plotData['ip']/float(plotData['ip'].sum())*100
# plotData = plotData.sort_values(by = ['ip'],ascending=False)
# print(plotData.head(n=10))
# dataFrame = dataFrame[(dataFrame['status'] == 'success')]
# plotData = dataFrame.groupby(['country'],sort=False).count().fillna(0).reset_index()[['country','ip']]
# plotData = plotData.sort_values(by = ['ip'],ascending=False)
# # plotData['ip'] = plotData['ip']/float(plotData['ip'].sum())*100
# print(plotData.head(n=10))
# # To discovery country wise information on prtocol port
# dataFrame = dataFrame[(dataFrame['status'] == 'success')]
# protocol_port_list = dataFrame.sort_values(by = ['Protocol, Port'],ascending=True)['Protocol, Port'].unique().tolist()
# for protocol_port in protocol_port_list:
# df = dataFrame[(dataFrame['Protocol, Port'] != protocol_port) ]
# plotData = df.groupby(['country'],sort=False).count().fillna(0).reset_index()[['country','ip']]
# plotData['ip'] = plotData['ip']/float(plotData['ip'].sum())*100
# plotData = plotData.sort_values(by = ['ip'],ascending=False)
# print(protocol_port)
# print(plotData.head())
# # To print the AS based distribution Total
# # dataFrame = dataFrame[dataFrame['status'] == 'success']
# plotData = dataFrame.groupby(['AS'],sort=False).count().fillna(0).reset_index()[['AS','ip']]
# # plotData['ip'] = plotData['ip']/float(plotData['ip'].sum())*100
# plotData = plotData.sort_values(by = ['ip'],ascending=False)
# print(plotData.head(n=10))
# print(plotData.shape)
# # To print the AS based distribution
# df = dataFrame[(dataFrame['status'] == 'success')]
# plotData = df.groupby(['AS'],sort=False).count().fillna(0).reset_index()[['AS','ip']]
# plotData['ip'] = plotData['ip']/float(plotData['ip'].sum())*100
# plotData = plotData.sort_values(by = ['ip'],ascending=False)
# print(plotData.head(n=10))
# # To find Discovery Percentage AS
# df = dataFrame[(dataFrame['status'] == 'success')]
# plotData = df.groupby(['AS'],sort=False).count().fillna(0).reset_index()[['AS','ip']]
# plotData = plotData.sort_values(by = ['ip'],ascending=False)
# print(plotData.head(n=10))
# top_AS_list = plotData.head(n=10)['AS'].to_list()
# df = df[df['AS'].isin(top_AS_list)]
# plotData = df.groupby(['AS'],sort=False).count().fillna(0).reset_index()[['AS','ip']]
# # plotData['ip'] = plotData['ip']/float(plotData['ip'].sum())*100
# plotData = plotData.sort_values(by = ['ip'],ascending=False)
# print(plotData)
# # To discovery AS wise information on prtocol port
# dataFrame = dataFrame[(dataFrame['status'] == 'success')]
# protocol_port_list = dataFrame.sort_values(by = ['Protocol, Port'],ascending=True)['Protocol, Port'].unique().tolist()
# for protocol_port in protocol_port_list:
# df = dataFrame[(dataFrame['Protocol, Port'] != protocol_port) ]
# plotData = df.groupby(['AS'],sort=False).count().fillna(0).reset_index()[['AS','ip']]
# plotData['ip'] = plotData['ip']/float(plotData['ip'].sum())*100
# plotData = plotData.sort_values(by = ['ip'],ascending=False)
# print(protocol_port)
# print(plotData.head(n=10))
# # AS categorisation
# df = dataFrame.groupby(['asn_category1L1L2'],sort=False).count().fillna(0).reset_index()[['asn_category1L1L2','ip']]
# df['ip'] = df['ip']/float(df['ip'].sum())*100
# plotData = df.sort_values(by = ['ip'],ascending=False)
# plotData.to_csv(str(processed_data_dir.resolve())+"/"+"AS_cat_total.csv", sep='\t', index=False)
print(file_name_prefix)
# dataFrame = dataFrame[dataFrame['status']=='success']
df = dataFrame.groupby(['asn_category1L1L2'],sort=False).count().fillna(0).reset_index()[['asn_category1L1L2','ip']]
df['ip'] = df['ip']/float(df['ip'].sum())*100
df = df.sort_values(by = ['ip'],ascending=False)
print(df.head(n=10))
as_cat_list=df['asn_category1L1L2'].unique().tolist()
for as_cat in as_cat_list:
print(as_cat)
print(len(dataFrame[dataFrame['asn_category1L1L2']==as_cat]['AS'].unique().tolist()))
# # AS categorisation
# df = dataFrame[(dataFrame['status'] == 'success')].groupby(['asn_category1L1L2'],sort=False).count().fillna(0).reset_index()[['asn_category1L1L2','ip']]
# df['ip'] = df['ip']/float(df['ip'].sum())*100
# plotData = df.sort_values(by = ['ip'],ascending=False)
# plotData.to_csv(str(processed_data_dir.resolve())+"/"+"AS_cat_success.csv", sep='\t', index=False)
# # AS categorisation
# top_AS_category = dataFrame[(dataFrame['status'] == 'success')].groupby(['asn_category1L1L2'],sort=False).count().fillna(0).reset_index()[['asn_category1L1L2','ip']].sort_values(by = ['ip'],ascending=False).head(1)['asn_category1L1L2'].to_list()[0]
# print(top_AS_category)
# df = dataFrame[(dataFrame['status'] == 'success') & (dataFrame['asn_category1L1L2'] == top_AS_category)]
# plotData = df.groupby(['Protocol, Port'],sort=False).count().fillna(0).reset_index()[['Protocol, Port','ip']]
# plotData['ip'] = plotData['ip']/float(plotData['ip'].sum())*100
# plotData = plotData.sort_values(by = ['ip'],ascending=False)
# print(plotData.head())
# Check io-timeout and country
# print(file_name_prefix)
# dataFrame = dataFrame[(dataFrame['status'] == 'io-timeout')]
# plotData = dataFrame.groupby(['country'],sort=False).count().fillna(0).reset_index()[['country','ip']]
# plotData = plotData.sort_values(by = ['ip'],ascending=False)
# plotData['ip'] = plotData['ip']/float(plotData['ip'].sum())*100
# print(plotData.head(n=10))
# # # Check io-timeout and AS
# print(file_name_prefix)
# dataFrame = dataFrame[(dataFrame['status'] == 'io-timeout')]
# plotData = dataFrame.groupby(['AS'],sort=False).count().fillna(0).reset_index()[['AS','ip']]
# plotData = plotData.sort_values(by = ['ip'],ascending=False)
# plotData['ip'] = plotData['ip']/float(plotData['ip'].sum())*100
# print(plotData.head(n=10))
# df = dataFrame[(dataFrame['status'] == 'io-timeout')]
# plotData = df.groupby(['AS'],sort=False).count().fillna(0).reset_index()[['AS','ip']]
# plotData = plotData.sort_values(by = ['AS'],ascending=False)
# plotData = plotData[plotData['ip'] > 50]
# AS_list_io = plotData['AS'].to_list()
# print(plotData)
# # plotData.to_csv(str(processed_data_dir.resolve())+"/"+file_name_prefix+"AS_io.csv", sep='\t', index=False)
# df = dataFrame[dataFrame['AS'].isin(AS_list_io)]
# plotData = df.groupby(['AS'],sort=False).count().fillna(0).reset_index()[['AS','ip']]
# # plotData['ip'] = plotData['ip']/float(plotData['ip'].sum())*100
# plotData = plotData.sort_values(by = ['AS'],ascending=False)
# print(plotData)
# # plotData.to_csv(str(processed_data_dir.resolve())+"/"+file_name_prefix+"AS.csv", sep='\t', index=False)
# AS_list = ['58519','44002','41495','133481','24547','210','14618','1312','18126','23848','24921','16509','2527','46844','24444','24445','61967','15557']
# # AS_list = [58519,44002,41495,133481,24547,210,14618,1312,18126,23848,24921,16509,2527,46844,24444,24445,61967,15557]
# df = dataFrame[dataFrame['AS'].isin(AS_list)]
# plotData = df.groupby(['AS'],sort=False).count().fillna(0).reset_index()[['AS','ip']]
# # plotData['ip'] = plotData['ip']/float(plotData['ip'].sum())*100
# plotData = plotData.sort_values(by = ['AS'],ascending=False)
# print(plotData)
# print(file_name_prefix)
# AS_list = ['58519','44002','41495','133481','24547','210','14618','1312','18126','23848','24921','16509','2527','46844','24444','24445','61967','15557']
# for ASN in AS_list:
# # AS_list = [58519,44002,41495,133481,24547,210,14618,1312,18126,23848,24921,16509,2527,46844,24444,24445,61967,15557]
# df = dataFrame[dataFrame['AS']== ASN]
# if(df.shape[0]>=1):
# print(df.head(n=1)[['AS','asn_category1L1L2']])
# plotData = df.groupby(['status'],sort=False).count().unstack('status').fillna(0)
# # plotData = df.groupby(['AS'],sort=False).count().fillna(0).reset_index()[['AS','ip']]
# # plotData['ip'] = plotData['ip']/float(plotData['ip'].sum())*100
# # plotData = plotData.sort_values(by = ['AS'],ascending=False)
# print(plotData['AS'])
# print(file_name_prefix)
# # AS_list = [58519,44002,41495,133481,24547,210,14618,1312,18126,23848,24921,16509,2527,46844,24444,24445,61967,15557]
# df = dataFrame[dataFrame['status'] != "success"]
# # print(df.head(n=1)[['status','error']])
# # plotData = df[df['status'] == 'io-timeout'].groupby(['error'],sort=False).count().unstack('error').fillna(0)
# # plotData = df.groupby(['AS'],sort=False).count().fillna(0).reset_index()[['AS','ip']]
# # plotData['ip'] = plotData['ip']/float(plotData['ip'].sum())*100