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Chrome Extension for Detection and Prevention of Phishing Websites and Related Attacks by Using Machine Learning and Deep Learning
Abstract
Phishing is a type of cyber-attack where a fraudulent message is sent by an attacker in order to deceive an individual into revealing sensitive information. The fraudulent message is often designed to mimic a legitimate website or organization, and the attacker can observe the victim's activity on the site. According to the FBI's Internet Crime Complaint Centre, phishing incidents have been recorded at more than twice the rate of any other type of computer crime. In 2022, phishing attacks were the most common type of attack carried out by cybercriminals. The number of phishing attacks reached an alltime high in 2021, with 300,000 attacks recorded in December alone, representing a more than threefold increase from less than 2 years prior. Our aim is to create a chrome extension to detect the Phishing websites and alert the user using machine learning techniques whenever a user visits the website. Whenever a user opens a website on his browser, the extension will work in the background, if the website is detected as phishing website, the user will be alerted with an alert pop up box with an ‘OK’ button. Along with it, the owner of the original website will be informed via email. If the website is not detected as phishing website, then no action will be taken. The project aims to gather URLs from multiple sources, including UCI machine learning repository, Kaggle, Phish Tank, and Alexa URL. We have used various machine learning algorithms along with deep learning algorithms to train the model and it was evaluated on the basis accuracy and on the basis of evaluation function such as recall, F1score, precision. The train/test split rule for dataset were 80/20 respectively. The browser extension was written in JavaScript. The extension contains content.js file which extracts features of URL such as browser popup, use of frames, web traffic, etc. and then applied the algorithm to detect whether the website is legitimate or not.
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DOI: https://doi.org/10.37628/ijocspl.v8i2.885
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