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Malware Detection in Web Browser Plugins Using API Calls with Permissions


Affiliations
1 Department of Computer Science, New Mexico Institute of Mining and Technology, Boise State University, United States
2 Department of Computer Science, Boise State University, United States
 

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.
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  • Malware Detection in Web Browser Plugins Using API Calls with Permissions

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Authors

Mohammad Sahinur Hossen
Department of Computer Science, New Mexico Institute of Mining and Technology, Boise State University, United States
Rakibul Islam
Department of Computer Science, New Mexico Institute of Mining and Technology, Boise State University, United States
Md Nasef Ur Rahman Chowdhury
Department of Computer Science, New Mexico Institute of Mining and Technology, Boise State University, United States
Ahshanul Haque
Department of Computer Science, New Mexico Institute of Mining and Technology, Boise State University, United States
Qudrat E Alahy Ratul
Department of Computer Science, Boise State University, United States

Abstract


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.

References