Detecting Phishing Websites with Random Forest

Shinelle Hutchinson; Zhaohe Zhang; Qingzhong Liu

Machine Learning and Intelligent Communications. Third International Conference, MLICOM 2018, Hangzhou, China, July 6-8, 2018, Proceedings

Research Article

Detecting Phishing Websites with Random Forest

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@INPROCEEDINGS{10.1007/978-3-030-00557-3_46,
    author={Shinelle Hutchinson and Zhaohe Zhang and Qingzhong Liu},
    title={Detecting Phishing Websites with Random Forest},
    proceedings={Machine Learning and Intelligent Communications. Third International Conference, MLICOM 2018, Hangzhou, China, July 6-8, 2018, Proceedings},
    proceedings_a={MLICOM},
    year={2018},
    month={10},
    keywords={Phishing Random forest Classification Website Detection},
    doi={10.1007/978-3-030-00557-3_46}
}

Shinelle Hutchinson
Zhaohe Zhang
Qingzhong Liu
Year: 2018
Detecting Phishing Websites with Random Forest
MLICOM
Springer
DOI: 10.1007/978-3-030-00557-3_46

Shinelle Hutchinson¹^,*, Zhaohe Zhang¹^,*, Qingzhong Liu¹^,*

1: Sam Houston State University

*Contact email: sdh053@shsu.edu, zxz003@shsu.edu, liu@shsu.edu

Abstract

Phishing has been a widespread issue for many years, claiming countless victims, some of which have not even realized that they fell prey. The sole purpose of phishing is to obtain sensitive information from its victims. There have yet to be a consensus on the best way to detect phishing. In this paper, we analyze web-based phishing detection by using Random Forest. Some important URL features are identified and our study shows that the detection performance with feature selection is improved.

Keywords: Phishing Random forest Classification Website Detection

Published: 2018-10-12
Appears in: SpringerLink

: http://dx.doi.org/10.1007/978-3-030-00557-3_46

Detecting Phishing Websites with Random Forest

Abstract

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