A Maximum Entropy Classification Scheme for Phishing Detection using Parsimonous Features

Asani, E. Oluwatobi and Omotosho, Adebayo and Danquah, Paul A. and Ayoola, Joyce and Ayegba, Peace and Longe, Olumide B (2021) A Maximum Entropy Classification Scheme for Phishing Detection using Parsimonous Features. TELKOMNIKA Telecommunication, Computing, Electronics and Control, 19 (5). pp. 1707-1714. ISSN 1693-6930

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Abstract

Over the years, electronic mail (e-mail) has been the target of several malicious attacks. Phishing is one of the most recognizable forms of manipulation aimed at e-mail users and usually, employs social engineering to trick innocent users into supplying sensitive information into an imposter website. Attacks from phishing emails can result in the exposure of confidential information, financial loss, data misuse, and others. This paper presents the implementation of a maximum entropy (ME) classification method for an efficient approach to the identification of phishing emails. Our result showed that maximum entropy with parsimonious feature space gives a better classification precision than both the Naïve Bayes and support vector machine (SVM)

Item Type:	Article
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	Faculty of Engineering, Science and Mathematics > School of Electronics and Computer Science
Depositing User:	EMMANUEL ASANI
Date Deposited:	15 Jan 2024 07:54
Last Modified:	15 Jan 2024 07:54
URI:	https://eprints.lmu.edu.ng/id/eprint/4525

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