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Alahy Ratul, Qudrat E
- Malware Detection in Web Browser Plugins Using API Calls with Permissions
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Authors
Mohammad Sahinur Hossen
1,
Rakibul Islam
1,
Md Nasef Ur Rahman Chowdhury
1,
Ahshanul Haque
1,
Qudrat E Alahy Ratul
2
Affiliations
1 Department of Computer Science, New Mexico Institute of Mining and Technology, Boise State University, US
2 Department of Computer Science, Boise State University, US
1 Department of Computer Science, New Mexico Institute of Mining and Technology, Boise State University, US
2 Department of Computer Science, Boise State University, US
Source
International Journal of Advanced Networking and Applications, Vol 14, No 6 (2023), Pagination: 5672-5677Abstract
With the exponential growth of internet users, web browsers play an essential role in gathering knowledge, social networking etc. Browser plugin/add-on is a unique feature of modern browsers that allows for adding new gimmicks to the browser functionality. Although this tool is handy, it poses a significant risk as it can collect and store users browsing history, passwords and more. Hence, attackers can try injecting malicious browser add-ons that can utilize security loopholes wherein the attacker may access user-critical data on the host device. The Smart Extension Malware Detector (SEMD), a reliable browser malware detection system that relies on extension development API calls and privileges using outfit machine learning approaches, was suggested and created by us. The research outcomes demonstrate that the SEMD model outperformed peer models while lowering the difficulty of the detection procedure.Keywords
Malware Detection, Browser Add-Ons, Machine Learning.Full Text
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