| Title | WebSecAsst - A Machine Learning based Chrome Extension |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Gurjar, Neelam Singh, S R, Sudheendra S, Kumar, Chejarla Santosh, K. S, Krishnaveni |
| Conference Name | 2021 6th International Conference on Communication and Electronics Systems (ICCES) |
| Keywords | Browsers, Chrome Extension, compositionality, Data models, data privacy, Human Behavior, machine learning, Metrics, phishing, pubcrawl, resilience, Resiliency, security, SHAP, Software, Viruses (medical), Web Browser Security, web security, Xgboost |
| Abstract | A browser extension, also known as a plugin or an addon, is a small software application that adds functionality to a web browser. However, security threats are always linked with such software where data can be compromised and ultimately trust is broken. The proposed research work jas developed a security model named WebSecAsst, which is a chrome plugin relying on the Machine Learning model XGBoost and VirusTotal to detect malicious websites visited by the user and to detect whether the files downloaded from the internet are Malicious or Safe. During this detection, the proposed model preserves the privacy of the user's data to a greater extent than the existing commercial chrome extensions. |
| DOI | 10.1109/ICCES51350.2021.9488953 |
| Citation Key | gurjar_websecasst_2021 |
WebSecAsst - A Machine Learning based Chrome Extension