Phishing Classifier Using Machine Learning

Abstract

This paper proposes a website phishing classifier using machine learning techniques. The proposed classifier uses a feature-based approach to extract relevant features from website URLs and contents. The extracted features are then used to train a classification model based on various machine learning algorithms such as Random Forest, Support Vector Machine, and Naive Bayes. The performance of the proposed classifier is evaluated using a publicly available dataset of phishing and legitimate websites. The experimental results show that the proposed classifier achieves high accuracy, precision, and recall in detecting phishing websites. The proposed classifier can be used as an effective tool to detect phishing websites in real-time and prevent users from falling victim to phishing attacks.