Welcome to the InfoSci Platform
Phish-Shelter: A Novel Anti-Phishing Browser Using Fused Machine Learning

Phish-Shelter: A Novel Anti-Phishing Browser Using Fused Machine Learning

Rizwan Ur Rahman, Lokesh Yadav, Deepak Singh Tomar, Deepak Singh Tomar
Copyright: © 2022 |Volume: 15 |Issue: 1 |Pages: 23
ISSN: 1938-7857|EISSN: 1938-7865|EISBN13: 9781683180340|DOI: 10.4018/JITR.2022010104
Cite Article Cite Article

MLA

Rahman, Rizwan Ur, et al. "Phish-Shelter: A Novel Anti-Phishing Browser Using Fused Machine Learning." JITR vol.15, no.1 2022: pp.1-23. http://doi.org/10.4018/JITR.2022010104

APA

Rahman, R. U., Yadav, L., Tomar, D. S., & Tomar, D. S. (2022). Phish-Shelter: A Novel Anti-Phishing Browser Using Fused Machine Learning. Journal of Information Technology Research (JITR), 15(1), 1-23. http://doi.org/10.4018/JITR.2022010104

Chicago

Rahman, Rizwan Ur, et al. "Phish-Shelter: A Novel Anti-Phishing Browser Using Fused Machine Learning," Journal of Information Technology Research (JITR) 15, no.1: 1-23. http://doi.org/10.4018/JITR.2022010104

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

Phishing attack is a deceitful attempt to steal the confidential data such as credit card information, and account passwords. In this paper, Phish-Shelter, a novel anti-phishing browser is developed, which analyzes the URL and the content of phishing page. Phish-Shelter is based on combined supervised machine learning model.Phish-Shelter browser uses two novel feature set, which are used to determine the web page identity. The proposed feature sets include eight features to evaluate the obfuscation-based rule, and eight features to identify search engine. Further, we have taken eleven features which are used to discover contents, and blacklist based rule. Phish-Shelter exploited matching identity features, which determines the degree of similarity of a URL with the blacklisted URLs. Proposed features are independent from third-party services such as web browser history or search engines result. The experimental results indicate that, there is a significant improvement in detection accuracy using proposed features over traditional features.