| Title | Browser Extension For A Safe Browsing Experience |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Fargose, Rehan, Gaonkar, Samarth, Jadhav, Paras, Jadiya, Harshit, Lopes, Minal |
| Conference Name | 2022 International Conference on Computing, Communication, Security and Intelligent Systems (IC3SIS) |
| Date Published | jun |
| Keywords | browser extension, Browsers, compositionality, Human Behavior, machine learning, machine learning algorithms, Metrics, pubcrawl, Random access memory, resilience, Resiliency, security, Software, software reliability, Uniform resource locators, Web Browser Security |
| Abstract | Due to the rise of the internet a business model known as online advertising has seen unprecedented success. However, it has also become a prime method through which criminals can scam people. Often times even legitimate websites contain advertisements that are linked to scam websites since they are not verified by the website's owners. Scammers have become quite creative with their attacks, using various unorthodox and inconspicuous methods such as I-frames, Favicons, Proxy servers, Domains, etc. Many modern Anti-viruses are paid services and hence not a feasible option for most users in 3rd world countries. Often people don't possess devices that have enough RAM to even run such software efficiently leaving them without any options. This project aims to create a Browser extension that will be able to distinguish between safe and unsafe websites by utilizing Machine Learning algorithms. This system is lightweight and free thus fulfilling the needs of most people looking for a cheap and reliable security solution and allowing people to surf the internet easily and safely. The system will scan all the intermittent URL clicks as well, not just the main website thus providing an even greater degree of security. |
| DOI | 10.1109/IC3SIS54991.2022.9885551 |
| Citation Key | fargose_browser_2022 |
Browser Extension For A Safe Browsing Experience