Phishing Website Detector using Machine Learning - Technical & Engineering Guide
1. Introduction
1.1 Purpose
This guide outlines the design and implementation of a Phishing Website Detector using Machine Learning. The project aims to identify and classify phishing websites based on their URL structure, content, and associated features.
1.2 Scope
This project is intended for cybersecurity teams, developers, and IT professionals to enhance web security by automatically detecting phishing websites and reducing potential threats.
1.3 Definitions & Acronyms
|
Acronym |
Definition |
|
URL |
Uniform Resource Locator, the address of a web resource. |
|
ML |
Machine Learning, a subset of artificial intelligence. |
|
Feature |
An attribute or characteristic used as input for ML models. |
|
TPR |
True Positive Rate, the rate of correctly identified phishing websites. |
2. System Architecture
The architecture of the Phishing Website Detector includes:
- **Data Collection**: Scrape URLs from phishing databases and legitimate
sources.
- **Feature Extraction**: Extract characteristics from URLs and website
content.
- **Model Training**: Train machine learning models on labeled data.
- **Prediction Engine**: Use the trained model to classify URLs.
- **Interface**: Provide a web-based or command-line interface for users.
3. Key Features
3.1 URL Analysis
Examines the structure, length, and patterns within URLs for phishing indicators.
3.2 Content Analysis
Analyzes webpage content such as keywords, form fields, and scripts.
3.3 Machine Learning Models
Supports multiple models including decision trees, random forests, and neural networks.
4. Implementation Steps
1. **Setup Environment**: Install required libraries
(scikit-learn, pandas, etc.).
2. **Data Collection**: Gather labeled datasets from sources like PhishTank and
Alexa.
3. **Feature Engineering**: Identify features such as URL length, presence of
special characters, and domain age.
4. **Model Training**: Train and validate models using datasets.
5. **Interface Development**: Build an application for URL input and detection.
6. **Testing**: Test with real-world URLs to assess performance.
7. **Deployment**: Deploy as a web service or desktop application.
5. Security Considerations
1. Regularly update datasets to include new phishing
techniques.
2. Ensure the system is resistant to adversarial input designed to evade
detection.
3. Protect user privacy and avoid storing sensitive URLs.
6. Tools and Technologies
- **Programming Language**: Python
- **Libraries**: scikit-learn, pandas, BeautifulSoup
- **Datasets**: PhishTank, OpenPhish, Alexa Top Sites
- **Model Options**: Decision Tree, Random Forest, SVM, Neural Networks
7. Testing and Validation
1. Measure accuracy, precision, recall, and TPR on test
datasets.
2. Evaluate performance with unseen real-world URLs.
3. Validate feature extraction accuracy.