Phishing Web-Site Detection Tool
Ojas Gurav
Vishwakarma University, Pune, India
Abstrct:
This project focuses on developing a machine learning model to classify URLs as either legitimate or phishing. Using a dataset containing various URL features, we trained a RandomForestClassifier to detect phishing attempts. The system was implemented in Python using libraries such as pandas, scikit-learn, and joblib. The trained model achieved an accuracy of 70% on the test set. The implementation involved preprocessing the dataset, training the model, saving and loading the model, and defining a function to classify new URLs based on extracted features. The system was tested thoroughly and successfully deployed to a production environment, demonstrating its reliability and effectiveness in phishing detection.
Keywords:
Phishing Detection, URL Classification, Python, Data Preprocessing, Model Deployment, Feature Extraction, Cybersecurity, Model Evaluation
Published on: 06-2024
Journal Name: Science Management Design Journal
Volume: 02
Issue: 02
Pages: 65-73
Month: June
Year: 2024