International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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Volume 174 - Issue 31 |
Published: Apr 2021 |
Authors: Vishal Jagtap, Vaibhav Shinde, Pratik Sapre, Kartik Karande, Ketaki Bhoyar |
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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
@article{ 10.5120/ijca2021921249, author = { Vishal Jagtap,Vaibhav Shinde,Pratik Sapre,Kartik Karande,Ketaki Bhoyar }, title = { Malicious Web Page Detection and Content Analysis }, journal = { International Journal of Computer Applications }, year = { 2021 }, volume = { 174 }, number = { 31 }, pages = { 10-13 }, doi = { 10.5120/ijca2021921249 }, publisher = { Foundation of Computer Science (FCS), NY, USA } }
%0 Journal Article %D 2021 %A Vishal Jagtap %A Vaibhav Shinde %A Pratik Sapre %A Kartik Karande %A Ketaki Bhoyar %T Malicious Web Page Detection and Content Analysis%T %J International Journal of Computer Applications %V 174 %N 31 %P 10-13 %R 10.5120/ijca2021921249 %I Foundation of Computer Science (FCS), NY, USA
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.