Abstract

The detection of malicious web pages is a complex engineering problem due to the ​dynamic nature of the information ​contained on the internet.Since the data stored on web-servers updates on a continuous basis, It is very hard to find and classify which links are malicious and which are not in ​real-time. Hence, brute-force checks (system-scans) and voting-based approaches (blacklisting) fail to capture the exhaustive list of malicious content on the internet. A machine learning based model is proposed which is able to classify the malicious links and content on the user’s device. It can later be applied in the forms: a web application, Android, iOS mobile applications and also browser extension which is able to give you a report of that link which you want to open on a device. The whole system performs a complete scan on that link and generates a report.

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