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:

Table 1: Basic features of individual TCP connections.

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

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())

Output

back,buffer_overflow,ftp_write,guess_passwd,imap,ipsweep,land,loadmodule,multihop,neptune,nmap,normal,perl,phf,pod,portsweep,rootkit,satan,smurf,spy,teardrop,warezclient,warezmaster.
duration: continuous.
protocol_type: symbolic.
service: symbolic.
flag: symbolic.
src_bytes: continuous.
dst_bytes: continuous.
land: symbolic.
wrong_fragment: continuous.
urgent: continuous.
hot: continuous.
num_failed_logins: continuous.
logged_in: symbolic.
num_compromised: continuous.
root_shell: continuous.
su_attempted: continuous.
num_root: continuous.
num_file_creations: continuous.
num_shells: continuous.
num_access_files: continuous.
num_outbound_cmds: continuous.
is_host_login: symbolic.
is_guest_login: symbolic.
count: continuous.
srv_count: continuous.
serror_rate: continuous.
srv_serror_rate: continuous.
rerror_rate: continuous.
srv_rerror_rate: continuous.
same_srv_rate: continuous.
diff_srv_rate: continuous.
srv_diff_host_rate: continuous.
dst_host_count: continuous.
dst_host_srv_count: continuous.
dst_host_same_srv_rate: continuous.
dst_host_diff_srv_rate: continuous.
dst_host_same_src_port_rate: continuous.
dst_host_srv_diff_host_rate: continuous.
dst_host_serror_rate: continuous.
dst_host_srv_serror_rate: continuous.
dst_host_rerror_rate: continuous.
dst_host_srv_rerror_rate: continuous.

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(',\n'):
    if(c.strip()):
       columns.append(c.strip())

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

# This code is modified by Susobhan Akhuli

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()

Output

	duration	protocol_type	service	flag	src_bytes	dst_bytes	land	wrong_fragment	urgent	hot	...	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	target	Attack Type
0	0	tcp	http	SF	181	5450	0	0	0	0	...	1.0	0.0	0.11	0.0	0.0	0.0	0.0	0.0	normal.	normal
1	0	tcp	http	SF	239	486	0	0	0	0	...	1.0	0.0	0.05	0.0	0.0	0.0	0.0	0.0	normal.	normal
2	0	tcp	http	SF	235	1337	0	0	0	0	...	1.0	0.0	0.03	0.0	0.0	0.0	0.0	0.0	normal.	normal
3	0	tcp	http	SF	219	1337	0	0	0	0	...	1.0	0.0	0.03	0.0	0.0	0.0	0.0	0.0	normal.	normal
4	0	tcp	http	SF	217	2032	0	0	0	0	...	1.0	0.0	0.02	0.0	0.0	0.0	0.0	0.0	normal.	normal

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.

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

# keep columns where there are more than 1 unique values and are numeric
ndf = df[[col for col in df.columns if df[col].nunique() > 1 and pd.api.types.is_numeric_dtype(df[col])]]

# Now calculate the correlation matrix
corr = ndf.corr()

plt.figure(figsize =(15, 12))
sns.heatmap(corr)
plt.show()

# This code is modified by Susobhan Akhuli

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

Colab Link

To get the colab notebook, click here. For dataset, click here.

mudgalabhay57

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Article Tags :

• AI-ML-DS
• Machine Learning
• AI-ML-DS With Python
• Machine Learning Projects

Practice Tags :

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