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Intrusion Detection System Using Machine Learning Algorithms

Last Updated : 10 Jul, 2025
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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.

Intrusion Detection System is a software application that detects network intrusion using various machine learning algorithms. IDS monitors a network or system for malicious activity and protects a computer network from unauthorized access by users, including perhaps insiders. The intrusion detector learning task is to build a predictive model (i.e., a classifier) capable of distinguishing between 'bad connections' (intrusion/attacks) and '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 other probing, e.g., port scanning.

You can download the dataset used in this project from Kaggle (the name of the dataset is Intrusion Detection System Using Machine Learning).

Dataset Description: Data files:

  • kddcup.names: A list of features.
  • kddcup.data_10_percent: A 10% subset of the dataset.
  • training_attack_types: A list of intrusion types.

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 namedescription 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 namedescription 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 servicecontinuous
diff_srv_rate % of connections to different servicescontinuous
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'' errorscontinuous
srv_rerror_rate % of connections that have ``REJ'' errorscontinuous
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: Importing and Setting Up the Data

Code: Importing libraries and reading features list.

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

from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from sklearn.naive_bayes import GaussianNB
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.svm import SVC
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score


Python 
with open("kddcup.names.txt", 'r') as f:
    print(f.read())

Output:

1


Appending columns to the dataset and adding a new column name 'target' to the dataset.

Python 
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))

Output:

42

Reading the 'attack_types' file.

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

Output:

2


Creating a dictionary of attack_types

Python 
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',
}


Reading the dataset('kddcup.data_10_percent_corrected") and adding Attack Type feature in the training dataset where attack type feature has 5 distinct values i.e. dos, normal, probe, r2l, u2r.

Python 
path = "kddcup.data_10_percent_corrected"
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:

3


Shape of dataframe and getting data type of each feature

Python 
df.shape

Output:

(494021, 43)

Finding missing values of all features.

Python 
df.isnull().sum()

Output:

4


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

Step 2: Data Exploration

Finding Categorical Features

Python 
# 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', 'protocol_type', 'flag']

Visualizing Categorical Features using bar graph

Python 
def bar_graph(feature):
    df[feature].value_counts().plot(kind="bar")


Python 
bar_graph('protocol_type')


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


Python 
bar_graph('logged_in')


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


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


Step 3: Data Preprocessing

Python 
df = df.drop(['target'], axis=1)
df = df.dropna(axis='columns')

# Filter numeric columns
ndf = df[[col for col in df.columns if df[col].nunique() > 1 and pd.api.types.is_numeric_dtype(df[col])]]

# Prepare feature matrix (X) and target variable (y)
y = df[['Attack Type']]
X = df.drop(['Attack Type'], axis=1)


Step 4: Splitting the Dataset

Python 
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)
print(f"Shape of X_train: {X_train.shape}, X_test: {X_test.shape}")
print(f"Shape of y_train: {y_train.shape}, y_test: {y_test.shape}")

Output:

Shape of X_train: (330994, 41), X_test: (163027, 41
Shape of y_train: (330994, 1), y_test: (163027, 1)

Step 5: Feature Encoding

Python 
# Map protocol_type to integers
pmap = {'icmp': 0, 'tcp': 1, 'udp': 2}
X_train['protocol_type'] = X_train['protocol_type'].map(pmap)
X_test['protocol_type'] = X_test['protocol_type'].map(pmap)

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


Step 6: Correlation Analysis

Python 
# Select numeric features for correlation matrix
X_train_numeric = X_train.select_dtypes(include=['float64', 'int64'])
corr = X_train_numeric.corr()

# Display heatmap of correlations
plt.figure(figsize=(12, 10))
sns.heatmap(corr, cmap='coolwarm', linewidths=0.5)
plt.title('Feature Correlation Heatmap (Training Set)')
plt.tight_layout()
plt.show()

Output:

5-min
Correlation Matrix

Step 7: Removing Highly Correlated Features

Python 
highly_correlated = ['num_root', 'srv_serror_rate', 'srv_rerror_rate', 'dst_host_srv_serror_rate',
                     'dst_host_serror_rate', 'dst_host_rerror_rate', 'dst_host_srv_rerror_rate',
                     'dst_host_same_srv_rate']
X_train.drop(columns=highly_correlated, axis=1, inplace=True)
X_test.drop(columns=highly_correlated, axis=1, inplace=True)


Dropping Columns that don't provide high value:

Python 
X_train.drop(['is_host_login', 'num_outbound_cmds'], axis=1, inplace=True)
X_test.drop(['is_host_login', 'num_outbound_cmds'], axis=1, inplace=True)

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


Correlation Matrix with transformed dataset:

Python 
X_train_numeric = X_train.select_dtypes(include=['float64', 'int64'])

corr = X_train_numeric.corr()

plt.figure(figsize=(12, 10))
sns.heatmap(corr, cmap='coolwarm', linewidths=0.5)
plt.title('Feature Correlation Heatmap (Training Set)')
plt.tight_layout()
plt.show()

Output:

6-min
Correlation Matrix with New Features

Step 8: Scaling the Data

Python 
sc = MinMaxScaler()

X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)

print(f"Shape of X_train after scaling: {X_train.shape}")
print(f"Shape of X_test after scaling: {X_test.shape}")

Output:

Shape of X_train after scaling: (330994, 30)
Shape of X_test after scaling: (163027, 30)

Step 9: Model Training and Test Accuracy

Python 
# Initialize classifiers
models = {
    "Naive Bayes": GaussianNB(),
    "Decision Tree": DecisionTreeClassifier(criterion="entropy", max_depth=4),
    "Random Forest": RandomForestClassifier(n_estimators=30),
    "SVM": SVC(gamma='scale'),
    "Logistic Regression": LogisticRegression(max_iter=1200000),
    "Gradient Boosting": GradientBoostingClassifier(random_state=0),
}

train_scores = []
test_scores = []
train_times = []
test_times = []

for name, model in models.items():
    print(f"\nTraining {name}...")
    start = time.time()
    model.fit(X_train, y_train.values.ravel())
    end = time.time()
    train_time = end - start

    start = time.time()
    y_pred_train = model.predict(X_train)
    y_pred_test = model.predict(X_test)
    end = time.time()
    test_time = end - start

    train_score = accuracy_score(y_train, y_pred_train) * 100
    test_score = accuracy_score(y_test, y_pred_test) * 100

    train_scores.append(train_score)
    test_scores.append(test_score)
    train_times.append(train_time)
    test_times.append(test_time)

    print(f"{name} - Train Accuracy: {train_score:.2f}%, Test Accuracy: {test_score:.2f}%")
    print(f"Training Time: {train_time:.4f}s, Testing Time: {test_time:.4f}s")

Output:

7
Training and Testing Accuracies

Conclusion

Naive Bayes:

  • Train Accuracy: 87.95%, Test Accuracy: 87.90%
  • This model performs decently but is not as good as others. It's good for a quick baseline but not the best choice for this problem.

Decision Tree:

  • Train Accuracy: 99.39%, Test Accuracy: 99.38%
  • This model is very accurate and performs almost equally well on both the training and test data. It’s great but might overfit the data (get too specialized).

Random Forest:

  • Train Accuracy: 100.00%, Test Accuracy: 99.97%
  • This model does perfectly on the training data and performs very well on the test data too. It's a strong contender but could be overfitting the training data.

SVM (Support Vector Machine):

  • Train Accuracy: 99.88%, Test Accuracy: 99.88%
  • SVM also performs almost perfectly on both training and test data. However, it takes a long time to train, which can be a downside for larger datasets.

Logistic Regression:

  • Train Accuracy: 99.36%, Test Accuracy: 99.36%
  • This model is simple, efficient, and performs really well with high accuracy, making it a good choice if you need something fast and reliable.

Gradient Boosting:

  • Train Accuracy: 99.91%, Test Accuracy: 99.91%
  • This model is another high performer with excellent accuracy on both training and test data. The downside is it takes a long time to train.


You can download the ipynb file for the complete code from here.


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