This project aims to detect phishing URLs using machine learning techniques. Phishing is a fraudulent attempt to obtain sensitive information by disguising as a trustworthy entity in electronic communications. This project leverages Python and various machine learning algorithms to identify and classify phishing URLs.
Data Collection: Gather a dataset of URLs labeled as phishing or legitimate. Feature Extraction: Extract relevant features from URLs, such as length, presence of special characters, and domain age. Model Training: Train machine learning models like Decision Trees, Random Forests, and Support Vector Machines to classify URLs. Evaluation: Evaluate the models using metrics like accuracy, precision, recall, and F1-score. Deployment: Implement the trained model in both desktop and web applications.
A Python Tkinter-based application for detecting phishing URLs. Hou to use? Execute python tkinter/main.py in the terminal.
A CGI-based web application for real-time phishing URL detection. Hou to use? Execute python -m http.server PORT_NUMBER --cgi in the terminal to run in local machine.
Project report is included to explain the functionality and usage of both the desktop and web applications.