Phishing Website Detection using Machine Learning

Abstract

Phishing attacks are a prevalent threat in cybersecurity, targeting users by tricking them into providing sensitive information through deceptive websites. This project aims to develop a robust method for detecting phishing websites by extracting and analyzing URL features using machine learning techniques. By leveraging various features such as URL length, special characters, subdomains, and suspicious keywords, we aim to build a predictive model that accurately identifies phishing URLs. This paper discusses the methodology, including data collection, preprocessing, feature selection, model training, and evaluation, ultimately highlighting the model's performance and potential applications in real-world scenarios. Keywords— Phishing Detection, Machine learning, Phish Tank