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Hossen, Mohammad Sahinur
- 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
References
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- Exploring the Benefits of Reinforcement Learning for Autonomous Drone Navigation and Control
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Authors
Affiliations
1 Department of Computer Science, New Mexico Tech, US
1 Department of Computer Science, New Mexico Tech, US
Source
International Journal of Advanced Networking and Applications, Vol 15, No 1 (2023), Pagination: 5808-5814Abstract
Drones are now used in a wide range of industries, including delivery services and agriculture. Notwithstanding, controlling robots in powerful conditions can be testing, particularly while performing complex assignments. Conventional strategies for drone mechanization depend on pre-customized directions, restricting their adaptability and versatility. Drones can learn from their interactions with their environment and improve their performance over time with the help of reinforcement learning (RL), which has emerged as a promising method for drone automation in recent years. This paper looks at how RL can be used to automate drones and how it can be used in different industries. In addition, the difficulties of RL-based drone automation and potential directions for future research are discussed in the paper.Keywords
Reinforcement Learning, Drone Automation, Machine Learning, Navigation, Obstacle Avoidance, Object Tracking, Safety, Reliability, Training Data, Dynamic Environments, Decision-Making.Full Text
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