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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
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