sis 20(27): e6

Research Article

Learning Model for Phishing Website Detection

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  • @ARTICLE{10.4108/eai.13-7-2018.163804,
        author={A. Suryan and C. Kumar and M. Mehta and R. Juneja and A. Sinha},
        title={Learning Model for Phishing Website Detection},
        journal={EAI Endorsed Transactions on Scalable Information Systems},
        volume={7},
        number={27},
        publisher={EAI},
        journal_a={SIS},
        year={2020},
        month={3},
        keywords={Information systems, phishing, machine learning, feature extraction, classification, dimensionality reduction, security},
        doi={10.4108/eai.13-7-2018.163804}
    }
    
  • A. Suryan
    C. Kumar
    M. Mehta
    R. Juneja
    A. Sinha
    Year: 2020
    Learning Model for Phishing Website Detection
    SIS
    EAI
    DOI: 10.4108/eai.13-7-2018.163804
A. Suryan1, C. Kumar1, M. Mehta1, R. Juneja1, A. Sinha1,*
  • 1: Jaypee Institute of Information Technology, Sector 62-A, Noida, UP, India
*Contact email: mailtoadwitiya@gmail.com

Abstract

Website portal empowered with information technology are of great importance in present scenario. With access to data all around the world, securing our information becomes an issue of topmost priority. Over the decade there have been numerous attacks by phishing websites and people have lost huge resources. Such malicious websites, also known as phishing website, steal information of authenticate users and carry out illegal transactions by misusing the personal information. Phishing website links and associated e-mails are sent to billions of users daily, thereby becoming a big concern for cyber security. In this paper, we address the phishing problem using machine learning approach applied on our proposed model, which uses 30 distinct features for phishing detection. We extracted multiple features from the website link and applied appropriate algorithms to classify the link as legitimate or phishing links.