Intrusion Detection System Using Machine Learning Algorithms

Problem Statement: The task is to build a network intrusion detector, a predictive model capable of distinguishing between bad connections, called intrusions or attacks, and good normal connections.

Introduction:
Intrusion Detection System is a software application to detect network intrusion using various machine learning algorithms.IDS monitors a network or system for malicious activity and protects a computer network from unauthorized access from users, including perhaps insider. The intrusion detector learning task is to build a predictive model (i.e. a classifier) capable of distinguishing between ‘bad connections’ (intrusion/attacks) and a ‘good (normal) connections’.

Attacks fall into four main categories:

• #DOS: denial-of-service, e.g. syn flood;
• #R2L: unauthorized access from a remote machine, e.g. guessing password;
• #U2R: unauthorized access to local superuser (root) privileges, e.g., various “buffer overflow” attacks;
• #probing: surveillance and another probing, e.g., port scanning.

Dataset Used : KDD Cup 1999 dataset

Dataset Description: Data files:

• kddcup.names : A list of features.
• kddcup.data.gz : The full data set
• kddcup.data_10_percent.gz : A 10% subset.
• kddcup.newtestdata_10_percent_unlabeled.gz
• kddcup.testdata.unlabeled.gz
• kddcup.testdata.unlabeled_10_percent.gz
• corrected.gz : Test data with corrected labels.
• training_attack_types : A list of intrusion types.
• typo-correction.txt : A brief note on a typo in the data set that has been corrected

Features:

feature name	description	type
duration	length (number of seconds) of the connection	continuous
protocol_type	type of the protocol, e.g. tcp, udp, etc.	discrete
service	network service on the destination, e.g., http, telnet, etc.	discrete
src_bytes	number of data bytes from source to destination	continuous
dst_bytes	number of data bytes from destination to source	continuous
flag	normal or error status of the connection	discrete
land	1 if connection is from/to the same host/port; 0 otherwise	discrete
wrong_fragment	number of “wrong” fragments	continuous
urgent	number of urgent packets	continuous

Table 1: Basic features of individual TCP connections.

feature name	description	type
hot	number of “hot” indicators	continuous
num_failed_logins	number of failed login attempts	continuous
logged_in	1 if successfully logged in; 0 otherwise	discrete
num_compromised	number of “compromised” conditions	continuous
root_shell	1 if root shell is obtained; 0 otherwise	discrete
su_attempted	1 if “su root” command attempted; 0 otherwise	discrete
num_root	number of “root” accesses	continuous
num_file_creations	number of file creation operations	continuous
num_shells	number of shell prompts	continuous
num_access_files	number of operations on access control files	continuous
num_outbound_cmds	number of outbound commands in an ftp session	continuous
is_hot_login	1 if the login belongs to the “hot” list; 0 otherwise	discrete
is_guest_login	1 if the login is a “guest”login; 0 otherwise	discrete

Table 2: Content features within a connection suggested by domain knowledge.

feature name	description	type
count	number of connections to the same host as the current connection in the past two seconds	continuous
	Note: The following features refer to these same-host connections.
serror_rate	% of connections that have “SYN” errors	continuous
rerror_rate	% of connections that have “REJ” errors	continuous
same_srv_rate	% of connections to the same service	continuous
diff_srv_rate	% of connections to different services	continuous
srv_count	number of connections to the same service as the current connection in the past two seconds	continuous
	Note: The following features refer to these same-service connections.
srv_serror_rate	% of connections that have “SYN” errors	continuous
srv_rerror_rate	% of connections that have “REJ” errors	continuous
srv_diff_host_rate	% of connections to different hosts	continuous

Table 3: Traffic features computed using a two-second time window.

Various Algorithms Applied: Gaussian Naive Bayes, Decision Tree, Random Forest, Support Vector Machine, Logistic Regression.

Approach Used: I have applied various classification algorithms that are mentioned above on the KDD dataset and compare there results to build a predictive model.

Step 1 – Data Preprocessing:

Code: Importing libraries and reading features list from ‘kddcup.names’ file.

import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import time

# reading features list
with open("..\\kddcup.names", 'r') as f:
    print(f.read())

Code: Appending columns to the dataset and adding a new column name ‘target’ to the dataset.

cols ="""duration,
protocol_type,
service,
flag,
src_bytes,
dst_bytes,
land,
wrong_fragment,
urgent,
hot,
num_failed_logins,
logged_in,
num_compromised,
root_shell,
su_attempted,
num_root,
num_file_creations,
num_shells,
num_access_files,
num_outbound_cmds,
is_host_login,
is_guest_login,
count,
srv_count,
serror_rate,
srv_serror_rate,
rerror_rate,
srv_rerror_rate,
same_srv_rate,
diff_srv_rate,
srv_diff_host_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_srv_diff_host_rate,
dst_host_serror_rate,
dst_host_srv_serror_rate,
dst_host_rerror_rate,
dst_host_srv_rerror_rate"""

columns =[]
for c in cols.split(', '):
    if(c.strip()):
       columns.append(c.strip())

columns.append('target')
print(len(columns))

Output:

42

Code: Reading the ‘attack_types’ file.

with open("..\\training_attack_types", 'r') as f:
    print(f.read())

Output:

back dos
buffer_overflow u2r
ftp_write r2l
guess_passwd r2l
imap r2l
ipsweep probe
land dos
loadmodule u2r
multihop r2l
neptune dos
nmap probe
perl u2r
phf r2l
pod dos
portsweep probe
rootkit u2r
satan probe
smurf dos
spy r2l
teardrop dos
warezclient r2l
warezmaster r2l

Code: Creating a dictionary of attack_types

attacks_types = {
    'normal': 'normal',
'back': 'dos',
'buffer_overflow': 'u2r',
'ftp_write': 'r2l',
'guess_passwd': 'r2l',
'imap': 'r2l',
'ipsweep': 'probe',
'land': 'dos',
'loadmodule': 'u2r',
'multihop': 'r2l',
'neptune': 'dos',
'nmap': 'probe',
'perl': 'u2r',
'phf': 'r2l',
'pod': 'dos',
'portsweep': 'probe',
'rootkit': 'u2r',
'satan': 'probe',
'smurf': 'dos',
'spy': 'r2l',
'teardrop': 'dos',
'warezclient': 'r2l',
'warezmaster': 'r2l',
}

Code: Reading the dataset(‘kddcup.data_10_percent.gz’) and adding Attack Type feature in the training dataset where attack type feature has 5 distinct values i.e. dos, normal, probe, r2l, u2r.

path = "..\\kddcup.data_10_percent.gz"
df = pd.read_csv(path, names = columns)

# Adding Attack Type column
df['Attack Type'] = df.target.apply(lambda r:attacks_types[r[:-1]])
df.head()

Code: Shape of dataframe and getting data type of each feature

df.shape

Output:

 (494021, 43)

Code: Finding missing values of all features.

df.isnull().sum()

Output:

duration                       0
protocol_type                  0
service                        0
flag                           0
src_bytes                      0
dst_bytes                      0
land                           0
wrong_fragment                 0
urgent                         0
hot                            0
num_failed_logins              0
logged_in                      0
num_compromised                0
root_shell                     0
su_attempted                   0
num_root                       0
num_file_creations             0
num_shells                     0
num_access_files               0
num_outbound_cmds              0
is_host_login                  0
is_guest_login                 0
count                          0
srv_count                      0
serror_rate                    0
srv_serror_rate                0
rerror_rate                    0
srv_rerror_rate                0
same_srv_rate                  0
diff_srv_rate                  0
srv_diff_host_rate             0
dst_host_count                 0
dst_host_srv_count             0
dst_host_same_srv_rate         0
dst_host_diff_srv_rate         0
dst_host_same_src_port_rate    0
dst_host_srv_diff_host_rate    0
dst_host_serror_rate           0
dst_host_srv_serror_rate       0
dst_host_rerror_rate           0
dst_host_srv_rerror_rate       0
target                         0
Attack Type                    0
dtype: int64

No missing value found, so we can further proceed to our next step.

Code: Finding Categorical Features

# Finding categorical features
num_cols = df._get_numeric_data().columns

cate_cols = list(set(df.columns)-set(num_cols))
cate_cols.remove('target')
cate_cols.remove('Attack Type')

cate_cols

Output:

 ['service', 'flag', 'protocol_type']

Visualizing Categorical Features using bar graph

Protocol type: We notice that ICMP is the most present in the used data, then TCP and almost 20000 packets of UDP type

logged_in (1 if successfully logged in; 0 otherwise): We notice that just 70000 packets are successfully logged in.

Target Feature Distribution:

Attack Type(The attack types grouped by attack, it’s what we will predict)

Code: Data Correlation – Find the highly correlated variables using heatmap and ignore them for analysis.

df = df.dropna('columns')# drop columns with NaN

df = df[[col for col in df if df[col].nunique() > 1]]# keep columns where there are more than 1 unique values

corr = df.corr()

plt.figure(figsize =(15, 12))

sns.heatmap(corr)

plt.show()

Output:

Code:

# This variable is highly correlated with num_compromised and should be ignored for analysis.
#(Correlation = 0.9938277978738366)
df.drop('num_root', axis = 1, inplace = True)

# This variable is highly correlated with serror_rate and should be ignored for analysis.
#(Correlation = 0.9983615072725952)
df.drop('srv_serror_rate', axis = 1, inplace = True)

# This variable is highly correlated with rerror_rate and should be ignored for analysis.
#(Correlation = 0.9947309539817937)
df.drop('srv_rerror_rate', axis = 1, inplace = True)

# This variable is highly correlated with srv_serror_rate and should be ignored for analysis.
#(Correlation = 0.9993041091850098)
df.drop('dst_host_srv_serror_rate', axis = 1, inplace = True)

# This variable is highly correlated with rerror_rate and should be ignored for analysis.
#(Correlation = 0.9869947924956001)
df.drop('dst_host_serror_rate', axis = 1, inplace = True)

# This variable is highly correlated with srv_rerror_rate and should be ignored for analysis.
#(Correlation = 0.9821663427308375)
df.drop('dst_host_rerror_rate', axis = 1, inplace = True)

# This variable is highly correlated with rerror_rate and should be ignored for analysis.
#(Correlation = 0.9851995540751249)
df.drop('dst_host_srv_rerror_rate', axis = 1, inplace = True)

# This variable is highly correlated with srv_rerror_rate and should be ignored for analysis.
#(Correlation = 0.9865705438845669)
df.drop('dst_host_same_srv_rate', axis = 1, inplace = True)

Output:

Code: Feature Mapping – Apply feature mapping on features such as : ‘protocol_type’ & ‘flag’.

# protocol_type feature mapping
pmap = {'icmp':0, 'tcp':1, 'udp':2}
df['protocol_type'] = df['protocol_type'].map(pmap)

Code:

# flag feature mapping
fmap = {'SF':0, 'S0':1, 'REJ':2, 'RSTR':3, 'RSTO':4, 'SH':5, 'S1':6, 'S2':7, 'RSTOS0':8, 'S3':9, 'OTH':10}
df['flag'] = df['flag'].map(fmap)

Output:

Code: Remove irrelevant features such as ‘service’ before modelling

df.drop('service', axis = 1, inplace = True)

Step 2 – Modelling

Code: Importing libraries and splitting the dataset

from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler

Code:

# Splitting the dataset
df = df.drop(['target', ], axis = 1)
print(df.shape)

# Target variable and train set
y = df[['Attack Type']]
X = df.drop(['Attack Type', ], axis = 1)

sc = MinMaxScaler()
X = sc.fit_transform(X)

# Split test and train data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.33, random_state = 42)
print(X_train.shape, X_test.shape)
print(y_train.shape, y_test.shape)

Output:

(494021, 31)
(330994, 30) (163027, 30)
(330994, 1) (163027, 1)

Apply various machine learning classification algorithms such as Support Vector Machines, Random Forest, Naive Bayes, Decision Tree, Logistic Regression to create different models.

Code: Python implementation of Gaussian Naive Bayes

# Gaussian Naive Bayes
from sklearn.naive_bayes import GaussianNB
from sklearn.metrics import accuracy_score

clfg = GaussianNB()
start_time = time.time()
clfg.fit(X_train, y_train.values.ravel())
end_time = time.time()
print("Training time: ", end_time-start_time)

Output:

Training time:  1.1145250797271729

Code:

start_time = time.time()
y_test_pred = clfg.predict(X_train)
end_time = time.time()
print("Testing time: ", end_time-start_time)

Output:

Testing time:  1.543299674987793

Code:

print("Train score is:", clfg.score(X_train, y_train))
print("Test score is:", clfg.score(X_test, y_test))

Output:

Train score is: 0.8795114110829804
Test score is: 0.8790384414851528

Code: Python implementation of Decision Tree

# Decision Tree
from sklearn.tree import DecisionTreeClassifier

clfd = DecisionTreeClassifier(criterion ="entropy", max_depth = 4)
start_time = time.time()
clfd.fit(X_train, y_train.values.ravel())
end_time = time.time()
print("Training time: ", end_time-start_time)

Output:

 Training time:  2.4408750534057617

start_time = time.time()
y_test_pred = clfd.predict(X_train)
end_time = time.time()
print("Testing time: ", end_time-start_time)

Output:

Testing time:  0.1487727165222168

print("Train score is:", clfd.score(X_train, y_train))
print("Test score is:", clfd.score(X_test, y_test))

Output:

Train score is: 0.9905829108684749
Test score is: 0.9905230421954646

Code: Python code implementation of Random Forest

from sklearn.ensemble import RandomForestClassifier

clfr = RandomForestClassifier(n_estimators = 30)
start_time = time.time()
clfr.fit(X_train, y_train.values.ravel())
end_time = time.time()
print("Training time: ", end_time-start_time)

Output:

 Training time:  17.084914684295654

start_time = time.time()
y_test_pred = clfr.predict(X_train)
end_time = time.time()
print("Testing time: ", end_time-start_time)

Output:

Testing time:  0.1487727165222168

print("Train score is:", clfr.score(X_train, y_train))
print("Test score is:", clfr.score(X_test, y_test))

Output:

Train score is: 0.99997583037759
Test score is: 0.9996933023364228

Code: Python implementation of Support Vector Classifier

from sklearn.svm import SVC

clfs = SVC(gamma = 'scale')
start_time = time.time()
clfs.fit(X_train, y_train.values.ravel())
end_time = time.time()
print("Training time: ", end_time-start_time)

Output:

Training time:  218.26840996742249

Code:

start_time = time.time()
y_test_pred = clfs.predict(X_train)
end_time = time.time()
print("Testing time: ", end_time-start_time)

Output:

Testing time:  126.5087513923645

Code:

print("Train score is:", clfs.score(X_train, y_train))
print("Test score is:", clfs.score(X_test, y_test))

Output:

Train score is: 0.9987552644458811
Test score is: 0.9987916112055059

Code: Python implementation of Logistic Regression

from sklearn.linear_model import LogisticRegression

clfl = LogisticRegression(max_iter = 1200000)
start_time = time.time()
clfl.fit(X_train, y_train.values.ravel())
end_time = time.time()
print("Training time: ", end_time-start_time)

Output:

Training time:  92.94222283363342

Code:

start_time = time.time()
y_test_pred = clfl.predict(X_train)
end_time = time.time()
print("Testing time: ", end_time-start_time)

Output:

Testing time:  0.09605908393859863

Code:

print("Train score is:", clfl.score(X_train, y_train))
print("Test score is:", clfl.score(X_test, y_test))

Output:

Train score is: 0.9935285835997028
Test score is: 0.9935286792985211

Code: Python implementation of Gradient Descent

from sklearn.ensemble import GradientBoostingClassifier

clfg = GradientBoostingClassifier(random_state = 0)
start_time = time.time()
clfg.fit(X_train, y_train.values.ravel())
end_time = time.time()
print("Training time: ", end_time-start_time)

Output:

Training time:  633.2290260791779

start_time = time.time()
y_test_pred = clfg.predict(X_train)
end_time = time.time()
print("Testing time: ", end_time-start_time)

Output:

Testing time:  2.9503915309906006

print("Train score is:", clfg.score(X_train, y_train))
print("Test score is:", clfg.score(X_test, y_test))

Output:

Train score is: 0.9979304760811374
Test score is: 0.9977181693829856

Code: Analyse the training and testing accuracy of each model.

names = ['NB', 'DT', 'RF', 'SVM', 'LR', 'GB']
values = [87.951, 99.058, 99.997, 99.875, 99.352, 99.793]
f = plt.figure(figsize =(15, 3), num = 10)
plt.subplot(131)
plt.bar(names, values)

Output:

Code:

names = ['NB', 'DT', 'RF', 'SVM', 'LR', 'GB']
values = [87.903, 99.052, 99.969, 99.879, 99.352, 99.771]
f = plt.figure(figsize =(15, 3), num = 10)
plt.subplot(131)
plt.bar(names, values)

Output:

Code: Analyse the training and testing time of each model.

names = ['NB', 'DT', 'RF', 'SVM', 'LR', 'GB']
values = [1.11452, 2.44087, 17.08491, 218.26840, 92.94222, 633.229]
f = plt.figure(figsize =(15, 3), num = 10)
plt.subplot(131)
plt.bar(names, values)

Output:

Code:

names = ['NB', 'DT', 'RF', 'SVM', 'LR', 'GB']
values = [1.54329, 0.14877, 0.199471, 126.50875, 0.09605, 2.95039]
f = plt.figure(figsize =(15, 3), num = 10)
plt.subplot(131)
plt.bar(names, values)

Output:

Implementation Link: https://github.com/mudgalabhay/intrusion-detection-system/blob/master/main.ipynb

Conclusion: The above analysis of different models states that the Decision Tree model best fits our data considering both accuracy and time complexity.

Links: The complete code is uploaded on my github account – https://github.com/mudgalabhay/intrusion-detection-system