🛡️ How to Build a Phishing URL Detector Using Machine Learning (Step-by-Step Guide)

In today’s digital age, phishing attacks are getting more frequent and more dangerous. One powerful way to stay ahead of them is by using Machine Learning to detect phishing URLs. In this blog post, we’ll walk you through how to build your own phishing URL detector—even if you’re new to ML or cybersecurity.

🚨 What Is a Phishing URL?

A phishing URL is a fake link designed to trick users into revealing sensitive information like passwords, bank details, or personal data. These URLs look legitimate but redirect users to malicious websites.

Examples:

secure-paypal.com.verify-login.com
gmail-reset-password.xyz
amazon-secure-check.com

💡 Why Use Machine Learning?

Traditional rule-based systems can’t keep up with new, disguised phishing URLs. Machine Learning, however, can learn patterns from data and detect new phishing links, even if they haven’t been seen before.

🛠️ Let’s Build: Step-by-Step Guide to Your Phishing Detector

1️⃣ Prerequisites

Basic Python knowledge
pandas, scikit-learn, matplotlib
Jupyter Notebook or Google Colab

2️⃣ Get the Dataset

Use the Phishing Website Dataset available on Kaggle.

Download and extract. phishing.csv

3️⃣ Load and Explore Data

import pandas as pd

data = pd.read_csv('phishing.csv')
print(data.head())
print(data.isnull().sum())

You’ll see features like UsingIP, LongURL, HTTPS, etc. The target column is usually named class or Result.

4️⃣ Split the Data

X = data.drop(['class'], axis=1)
y = data['class']

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

This splits the data: 70% for training and 30% for testing.

5️⃣ Train the Model

from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, classification_report

model = RandomForestClassifier()
model.fit(X_train, y_train)

y_pred = model.predict(X_test)
print("Accuracy:", accuracy_score(y_test, y_pred))
print(classification_report(y_test, y_pred))

You’ll get a performance report, including precision, recall, and F1-score.

6️⃣ Visualize the Confusion Matrix

import matplotlib.pyplot as plt
from sklearn.metrics import ConfusionMatrixDisplay

ConfusionMatrixDisplay.from_estimator(model, X_test, y_test)
plt.show()

This shows how well your model is classifying phishing vs. legitimate URLs.

✅ Bonus: Export Your Trained Model

import joblib
joblib.dump(model, 'phishing_model.pkl')

You can now use this “.pkl” file in a web app or REST API!

🧠 Tips to Improve the Model

Use advanced NLP on URLs
Include domain reputation scores
Combine URL data with page content features
Try deep learning models (e.g., LSTM)

🛡️ How to Stay Protected from Phishing Attacks

Even the best AI isn’t foolproof. Here’s what you should do:

Check the URL carefully before clicking.
Enable 2FA on all accounts.
Use trusted antivirus software.
Don’t trust emails asking for urgent action.
Train employees on phishing awareness.

🚀 Final Words

You’ve just built your first phishing detector using Machine Learning! Not only does this strengthen your portfolio (add it to GitHub!), but it also contributes to a safer internet.

Want to learn more about cybersecurity projects? Explore other blogs on HackingWit.com and follow us on Medium!