Which algorithms are used in this phishing website detection project?

The system implements and compares two algorithms: Logistic Regression and Random Forest. Random Forest achieved superior performance with 96.85% accuracy and 0.9957 ROC-AUC.

What is the accuracy of the phishing detection model?

The Random Forest model achieves 96.85% accuracy with 97.12% precision, 96.58% recall, and an F1-score of 96.85%, demonstrating excellent phishing detection capability.

Who can benefit from this phishing detection system?

Cybersecurity professionals, security analysts, web administrators, financial institutions, and organizations can use this system for proactive phishing detection and protection against credential theft.

What technologies are used to build this project?

The project is built using Python with Pandas, NumPy, Scikit-learn, and Matplotlib for machine learning. The web interface is developed using Flask with HTML, CSS, and JavaScript.

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Machine Learning

Phishing Website Detection Using Machine Learning

The Phishing Website Detection Using Machine Learning system is a comprehensive machine learning solution that detects phishing websites using 48 URL and page content features. This project is ideal for B.Tech, MCA, CSE, and engineering students as their final year project. It implements Logistic Regression and Random Forest with 5-fold cross-validation, achieving 96.85% accuracy and 0.9957 ROC-AUC.

The system utilizes the 'Phishing Legitimate Full' dataset containing 11,430 website instances with 48 features derived from URL structure and page content analysis. It provides a user-friendly web interface for data upload, exploratory data analysis, model training, and real-time phishing detection, enabling organizations to proactively protect against credential theft and financial fraud.

Python 3.8+ Machine Learning Random Forest Logistic Regression Scikit-learn Pandas NumPy Matplotlib Seaborn Flask HTML/CSS/JS

Key Features:

Multi-Model Phishing Detection
Random Forest (96.85% Accuracy)
Logistic Regression (94.90% Accuracy)
48 URL and Page Content Features
5-Fold Leak-Free Cross-Validation
Interactive Visualizations
Real-time URL Predictions
Model Performance Comparison
Feature Importance Analysis
Flask Web Application

Algorithms Used

📊 Logistic Regression

Linear classifier with L2 regularization, C=1.0, max_iter=1000, solver='lbfgs'

🎯 Accuracy: 94.90%

🌲 Random Forest

Ensemble learning with n_estimators=100, max_depth=10, class_weight='balanced'

🎯 Accuracy: 96.85%

🔧 Feature Engineering

48 features: UrlLength, NumDots, SubdomainLevel, PctExtResourceUrls, etc.

📊 Top Feature: UrlLength

📊 Cross-Validation

5-fold stratified CV with leak-free evaluation

📊 CV Mean: 96.69%

Methodology & Workflow

1 Data Collection
11,430 website instances with 48 features

2 Data Preprocessing
Cleaning, encoding, feature scaling (StandardScaler)

3 Exploratory Data Analysis
Statistical analysis and visualization of patterns

4 Model Training
Logistic Regression and Random Forest with 5-fold CV

5 Model Evaluation
Accuracy, Precision, Recall, F1-Score, ROC-AUC

6 Web Deployment
Flask web app with real-time phishing detection

Model Performance Comparison

Metric	Logistic Regression	Random Forest	Best
Test Accuracy	94.90%	96.85%	Random Forest
Test Precision	95.28%	97.12%	Random Forest
Test Recall	94.48%	96.58%	Random Forest
Test F1-Score	94.88%	96.85%	Random Forest
Test ROC-AUC	98.84%	99.57%	Random Forest
CV Mean Accuracy	94.72%	96.69%	Random Forest

Project Package Includes:

Complete Source Code Documentation (50+ pages) Video Tutorial Phishing Legitimate Dataset Flask Web App Model Files (Pickle) Visualizations 24/7 Expert Support

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