CheckPhish: Leveraging A Machine Learning Approach for Detecting Phishing Websites

Phishing is a cybercrime in which an attacker creates a fake website that appears to be a legitimate one to lure individuals into providing sensitive information such as credentials like usernames and passwords, bank account details, credit card details, and other personal details. With the increasing use of the internet for various purposes, phishing attacks have become a major concern for individuals, businesses, and organizations. Although there are several ways for the detection of phishing websites, phishers have found techniques to get around these methods. Machine learning is one of the finest methods for detecting these harmful activities. This is done so that machine learning algorithms can identify the characteristics that most phishing attacks have in common. This study provides machine learning-based methods for identifying phishing websites. The proposed approach uses a combination of features extracted from the website’s URL to train a machine-learning model that can classify a website as either safe or unsafe. To do that, we trained and tested eight machine-learning algorithms before selecting the best one by comparing accuracy, precision, recall, and F1 score. We have achieved an accuracy of 86.2% with the XGBoost classifier, which demonstrates how well our method works in identifying phishing sites.