TELKOMNIKA Telecommunication, Computing, Electronics and Control
A maximum entropy classification scheme for phishing detection using parsimonious features
Dublin Core
Title
TELKOMNIKA Telecommunication, Computing, Electronics and Control
A maximum entropy classification scheme for phishing detection using parsimonious features
A maximum entropy classification scheme for phishing detection using parsimonious features
Subject
Classification
Parsimonous features
Phishing
Social engineering
Parsimonous features
Phishing
Social engineering
Description
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).
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).
Creator
Emmanuel O. Asani, Adebayo Omotosho, Paul A. Danquah, Joyce A. Ayoola, Peace O. Ayegba, Olumide B. Longe
Source
http://journal.uad.ac.id/index.php/TELKOMNIKA
Date
Jul 13, 2021
Contributor
peri irawan
Format
pdf
Language
english
Type
text
Files
Collection
Citation
Emmanuel O. Asani, Adebayo Omotosho, Paul A. Danquah, Joyce A. Ayoola, Peace O. Ayegba, Olumide B. Longe, “TELKOMNIKA Telecommunication, Computing, Electronics and Control
A maximum entropy classification scheme for phishing detection using parsimonious features,” Repository Horizon University Indonesia, accessed October 31, 2025, https://repository.horizon.ac.id/items/show/4179.
A maximum entropy classification scheme for phishing detection using parsimonious features,” Repository Horizon University Indonesia, accessed October 31, 2025, https://repository.horizon.ac.id/items/show/4179.