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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control)
Peace O. Ayegba
Landmark University
Computer Science & IT

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A maximum entropy classification scheme for phishing detection using parsimonious features Emmanuel O. Asani; Adebayo Omotosho; Paul A. Danquah; Joyce A. Ayoola; Peace O. Ayegba; Olumide B. Longe
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 19, No 5: October 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v19i5.15981

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).
Co-Authors Adebayo Omotosho Emmanuel O. Asani Joyce A. Ayoola Olumide B. Longe Paul A. Danquah