WEB BROWSER EXTENSION FOR DETECTING COVID-19 THEMED MALICIOUS WEB CONTENT
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Date
2021
Journal Title
Journal ISSN
Volume Title
Publisher
FACULTY: MATHIMATICS AND COMPUTER SCIENCE DEPARTEMENT: COMPUTER SCIENCE - OPTION: RTIC
Abstract
In light of the spread of the Covid-19 virus and the necessity of using the Internet as remote work, malicious content on the web has become a global threat to web users, as its huge spread has made users vulnerable to all kinds of cyber-attacks that can be carried out behind websites. Several recent researches have been proposed to detect harmful content on the subject of COVID-19, however detection of COVID-19 themed malicious websites has not been entirely resolved until now.
In this work, we propose a chrome extension to detect malicious web content with the theme of COVID-19 by analyzing HTTP requests and responses. The extension relies on a list of malicious features built with a machine learning classifier, and it does more protection. The experimental results obtained demonstrate the effectiveness of our extension in detecting malicious content with the theme of COVID-19, which was demonstrated by the perfect accuracy score.
Description
Keywords
COVID-19, Malicious Websites, Web Browser Extension, Detection, Machine learning.