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July Edition 2021

International Journal of Applied Information Systems solicits high quality original research papers for the July 2021 Edition of the journal. The last date of research paper submission is June 15, 2021.

Phishing Detection in E-mails using Machine Learning

Srishti Rawal, Bhuvan Rawal, Aakhila Shaheen, Shubham Malik in Security

International Journal of Applied Information Systems
Year of Publication: 2017
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors:Srishti Rawal, Bhuvan Rawal, Aakhila Shaheen, Shubham Malik
10.5120/ijais2017451713
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  1. Srishti Rawal, Bhuvan Rawal, Aakhila Shaheen and Shubham Malik. Phishing Detection in E-mails using Machine Learning. International Journal of Applied Information Systems 12(7):21-24, October 2017. URL, DOI BibTeX

    @article{10.5120/ijais2017451713,
    	author = "Srishti Rawal and Bhuvan Rawal and Aakhila Shaheen and Shubham Malik",
    	title = "Phishing Detection in E-mails using Machine Learning",
    	journal = "International Journal of Applied Information Systems",
    	issue_date = "October 2017",
    	volume = 12,
    	number = 7,
    	month = "October",
    	year = 2017,
    	issn = "2249-0868",
    	pages = "21-24",
    	url = "http://www.ijais.org/archives/volume12/number7/1005-2017451713",
    	doi = "10.5120/ijais2017451713",
    	publisher = "Foundation of Computer Science (FCS), NY, USA",
    	address = "New York, USA"
    }
    

Abstract

Emails are widely used as a means of communication for personal and professional use. The information exchanged over mails is often sensitive and confidential such as banking information, credit reports, login details etc. This makes them valuable to cyber criminals who can use the information for malicious purposes. Phishing is a strategy used by fraudsters to obtain sensitive information from people by pretending to be from recognized sources. In a phished email, the sender can convince you to provide personal information under false pretenses. This experimentation considers the detection of a phished email as a classification problem and this paper describes the use of machine learning algorithms to classify emails as phished or ham. Maximum accuracy of 99. 87% is achieved in classification of emails using SVM and Random Forest classifier.

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Keywords

Phishing detection, SVM, ham, naive bayes, machine learning, email fraud, artificial intelligence