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Malicious Web Page Detection and Content Analysis
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 174 - Number 31 |
| Year of Publication: 2021 |
| Authors: Vishal Jagtap, Vaibhav Shinde, Pratik Sapre, Kartik Karande, Ketaki Bhoyar |
10.5120/ijca2021921249
|
Vishal Jagtap, Vaibhav Shinde, Pratik Sapre, Kartik Karande, Ketaki Bhoyar . Malicious Web Page Detection and Content Analysis. International Journal of Computer Applications. 174, 31 ( Apr 2021), 10-13. DOI=10.5120/ijca2021921249
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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