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iot_hass_service.py
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iot_hass_service.py
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##################################################################
# #
# IoT-HASS Service #
# ===================================== #
# #
# This is the main program for the IoT-HASS framework. It calls #
# different engines to perform IDS/IPS, Privacy Monitoring and #
# Device Management. I also perform a logging for all traffic #
# whether normal or attacks. #
##################################################################
# Importing external custom modules
import ips_engine as ips
import privacy_monitoring_engine as pmv
import device_management_engine as dm
# Importing the libraries
import numpy as np
import pandas as pd
#from scapy.all import *
#from scapy.utils import hexdump
from multiprocessing import Process
import socket
import struct
import textwrap
import datetime
import os
from datetime import timedelta
import statistics as stats
IPS = ips.IPS_Engine
DM = dm.DM_Engine
PMV = pmv.Privacy_Engine
# Function to format IP address as ipv4
def format_ip_to_ipv4(addr):
return '.'.join(map(str, addr))
# Function to extract the packet header information including source and target IPs
def ipv4_packet_header(data):
version_header_length = data[0]
version = version_header_length >> 4
header_length = (version_header_length & 15) * 4
ttl, proto, src, target = struct.unpack('! 8x B B 2x 4s 4s', data[:20])
return version, header_length, ttl, proto, format_ip_to_ipv4(src), format_ip_to_ipv4(target), data[header_length:]
# Function to retuen properly formatted mac address
def get_mac_address(bytes_addr):
bytes_addr = map('{:02x}'.format, bytes_addr)
return ':'.join(bytes_addr).upper()
# Function to unpack tcp segment
def unpack_tcp_segment(data):
(src_port, dest_port, sequence, acknowledgement, offset_reserved_flags) = struct.unpack('! H H L L H', data[:14])
offset = (offset_reserved_flags >> 12) * 4
flag_urg = (offset_reserved_flags & 32) >> 5
flag_ack = (offset_reserved_flags & 16) >> 4
flag_psh = (offset_reserved_flags & 8) >> 3
flag_rst = (offset_reserved_flags & 4) >> 2
flag_syn = (offset_reserved_flags & 2) >> 1
flag_fin = offset_reserved_flags & 1
return src_port, dest_port, sequence, acknowledgement, flag_urg, flag_ack, flag_psh, flag_rst, flag_syn, flag_fin, data[offset:]
# Function to unpack ethernet frame
def unpack_ethernet_frame(data):
dest_mac, src_mac, proto = struct.unpack('! 6s 6s H', data[:14])
return get_mac_address(dest_mac), get_mac_address(src_mac), socket.htons(proto), data[14:]
# Function to start and run both IDS and Privacy engine simultanuosly by using Python threading
def iot_hass(mean_biat, std_biat, max_biat, pkt_len_varience, std_idle, src, target, classifier, dest_mac, src_mac, raw_data):
print("IoT-HASS is Running Protecting Your Home Realtime Now!")
# Call the IPS Engine
p1 = Process(target = IPS.ips_engine(mean_biat, std_biat, max_biat, pkt_len_varience, std_idle, src, target, classifier))
p1.start()
# Call the privacy_protction_engine
p2 = Process(target = PMV.privacy_monitoring_engine(dest_mac, src_mac, raw_data, src, target))
p2.start()
def main():
print("Validating Connected IoT Devices!")
DM.dm_engine()
DM.block_all_ips()
# Importing the dataset
dataset = pd.read_csv('/home/pi/Software/IoT-HASS/CICIDS2017_Sample.csv')
X = dataset.iloc[:, :-1].values
y = dataset.iloc[:, 78].values
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, random_state = 0)
############## Start of Feature Scaling ###################
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
# Fitting Decision Tree Classification to the Training set
from sklearn.tree import DecisionTreeClassifier
classifier = DecisionTreeClassifier(criterion = 'entropy', random_state = 0)
classifier.fit(X_train, y_train)
# Feature Selection
from sklearn.feature_selection import SelectKBest, SelectPercentile, chi2
KBestSelector = SelectKBest(k=5)
KBestSelector = KBestSelector.fit(X_train, y_train)
X_train_FS = KBestSelector.transform(X_train)
names = dataset.iloc[:, :-1].columns.values[KBestSelector.get_support()]
scores = KBestSelector.scores_[KBestSelector.get_support()]
names_scores = list(zip(names, scores))
ns_df = pd.DataFrame(data= names_scores, columns=['Feat_Name', 'F_Score'])
ns_df_sorted = ns_df.sort_values(['F_Score', 'Feat_Name'])
#print(ns_df_sorted)
# Fit the model with the new reduced features
classifier.fit(X_train_FS, y_train)
# Predicting the Test set results
X_test_FS = KBestSelector.transform(X_test)
y_pred = classifier.predict(X_test_FS)
conn = socket.socket(socket.AF_PACKET, socket.SOCK_RAW, socket.ntohs(3))
# define array variables to hold time and statistics
TimeBetBwdPkts = 0
NumBwdPkts = 0
NumIdleFlow = 0
prev_fin_flag = 0
flow_idle_start_time = datetime.datetime.now()
flow_idle_end_time = datetime.datetime.now()
AllTimesBetBwdPkts = []
AllflowIdleTimes = []
AllPacketLengths = []
max_biat = 0
mean_biat = 0
std_biat = 0
pkt_len_varience = 0
std_idle = 0
while True:
raw_data, addr = conn.recvfrom(65535)
dest_mac, src_mac, eth_proto, data = unpack_ethernet_frame(raw_data)
# get packet length or size
packet_length = len(raw_data)
AllPacketLengths.append(packet_length)
# IPv4
if eth_proto == 8:
(version, header_length, ttl, proto, src, target, data) = ipv4_packet_header(data)
# TCP packet
if proto == 6:
(src_port, dest_port, sequence, acknowledgement, flag_urg, flag_ack, flag_psh, flag_rst, flag_syn, flag_fin, data) = unpack_tcp_segment(data)
# capture packet flow
# we will identifiy each flow by determining when src and dst ip change
# first capture the original src and dst IPs
prev_src_ip = src
prev_target_ip = target
if flag_fin == '1' and prev_fin_flag == '0':
flow_idle_start_time = datetime.datetime.now()
NumIdleFlow = NumIdleFlow + 1
elif flag_fin == '0' and prev_fin_flag == '1':
flow_idle_end_time = datetime.datetime.now()
else:
flow_idle_start_time = datetime.datetime.now()
flow_idle_end_time = datetime.datetime.now()
prev_fin_flag = flag_fin
flowIdleTime = (flow_idle_end_time - flow_idle_start_time).microseconds
AllflowIdleTimes.append(flowIdleTime)
LastTimeBwdPktSeen = datetime.datetime.now()
if(NumBwdPkts == 1):
TimeBetBwdPkts = 0
elif(NumBwdPkts > 1):
TimeBetBwdPkts = (datetime.datetime.now() - LastTimeBwdPktSeen).microseconds
else:
TimeBetBwdPkts = 0
NumBwdPkts = NumBwdPkts + 1
AllTimesBetBwdPkts.append(TimeBetBwdPkts)
# get statistics values for backwards packets
if sum(AllTimesBetBwdPkts) == 0:
mean_biat = 0
max_biat = 0
std_biat = 0
else:
mean_biat = stats.mean(AllTimesBetBwdPkts)
max_biat = max(AllTimesBetBwdPkts)
std_biat = stats.stdev(AllTimesBetBwdPkts)
if(sum(AllflowIdleTimes) > 0 and len(AllflowIdleTimes) > 1):
std_idle = stats.stdev(AllflowIdleTimes)
else:
std_idle = 0
if(sum(AllPacketLengths) > 0 and len(AllPacketLengths) > 1):
pkt_len_varience = stats.variance(AllPacketLengths)
else:
pkt_len_varience = 0
# Invoking iot_hass() function
iot_hass(mean_biat, std_biat, max_biat, pkt_len_varience, std_idle, src, target, classifier, dest_mac, src_mac, raw_data)
if __name__ =="__main__":
main()