Paper Title
Phishing URL Detection using Feature Extraction and Machine Learning

The idea behind this project is to implement machine learning techniques to identify phishing websites URLs. The final conclusion of the project will be the machine learning model which best predicts the malicious URLs. Phishing is a common attack on credulous people by making them disclose their unique information using websites which mimics legitimate websites. The objective of phishing website URLs is to steal the personal information like user’s name, saved passwords, credit card information and online banking details. Phishers use the websites which look almost visually and semantically similar to those of legitimate websites. As recent advancement in technology continues to grow and expand, phishing techniques have started to progress rapidly and this needs to be prevented by using anti-phishing techniques and mechanisms to detect phishing websites. Growing demand and advancement in Machine learning has proved to be the powerful technology used to strive against phishing attacks. This project aims to detect the features and selecting the right features which uniquely identifies a phishing website and differentiate between a phishing and legitimate website. Using those features we aim to develop machine learning models to detect phishing websites. Keywords - Phishing Website, Logistic Regression, Random Forest, Feature Extractions, Artificial Neural Network