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Comparative analysis of machine learning classifiers for phishing detection

In recent years, communication over the Internet has become the most effective media for leveraging social interactions during the COVID-19 pandemic. Nevertheless, the rapid increase use of digital platforms has led to a significant growth of Phishing Attacks. Phishing attacks are one of the most co...

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Main Authors:	Mohd Faizal, Ab Razak, Mohd Izham, Jaya, Ernawan, Ferda, Ahmad Firdaus, Zainal Abidin, Nugroho, Fajar Agung
Format:	Conference or Workshop Item
Language:	English English
Published:	Institute of Electrical and Electronics Engineers Inc. 2022
Subjects:	QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General)
Online Access:	http://umpir.ump.edu.my/id/eprint/39337/1/Comparative%20analysis%20of%20machine%20learning%20classifiers%20for%20phishing%20detection.pdf http://umpir.ump.edu.my/id/eprint/39337/2/Comparative%20analysis%20of%20machine%20learning%20classifiers%20for%20phishing%20detection_ABS.pdf http://umpir.ump.edu.my/id/eprint/39337/ https://doi.org/10.1109/ICICoS56336.2022.9930531
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id	my.ump.umpir.39337
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spelling	my.ump.umpir.393372023-11-20T07:29:08Z http://umpir.ump.edu.my/id/eprint/39337/ Comparative analysis of machine learning classifiers for phishing detection Mohd Faizal, Ab Razak Mohd Izham, Jaya Ernawan, Ferda Ahmad Firdaus, Zainal Abidin Nugroho, Fajar Agung QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) In recent years, communication over the Internet has become the most effective media for leveraging social interactions during the COVID-19 pandemic. Nevertheless, the rapid increase use of digital platforms has led to a significant growth of Phishing Attacks. Phishing attacks are one of the most common security issues in digital worlds that can affects both individual and organization in keeping their confidential information secure. Various modern approaches can be used to target an individual and trick them into leaking their sensitive information, which can later, purposely be used to harm the targeted victim or entire organization depending on the cybercriminal's intent and type of data leaked. This paper evaluates phishing detection by using Naïve Bayes, Simple Logistic, Random Forest, Ada Boost and MLP classifications. This study discusses the comparative analysis on the effectiveness of classification for detecting phishing attacks. The results indicated that the detection system trained with the Random Forest produce higher accuracy of 97.98% than another classifier method. Institute of Electrical and Electronics Engineers Inc. 2022-09 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/39337/1/Comparative%20analysis%20of%20machine%20learning%20classifiers%20for%20phishing%20detection.pdf pdf en http://umpir.ump.edu.my/id/eprint/39337/2/Comparative%20analysis%20of%20machine%20learning%20classifiers%20for%20phishing%20detection_ABS.pdf Mohd Faizal, Ab Razak and Mohd Izham, Jaya and Ernawan, Ferda and Ahmad Firdaus, Zainal Abidin and Nugroho, Fajar Agung (2022) Comparative analysis of machine learning classifiers for phishing detection. In: Proceedings - International Conference on Informatics and Computational Sciences; 6th International Conference on Informatics and Computational Sciences, ICICoS 2022, 28-29 September 2022 , Virtual, Online. pp. 84-88., 2022 (183902). ISSN 2767-7087 ISBN 978-166546099-6 https://doi.org/10.1109/ICICoS56336.2022.9930531
institution	Universiti Malaysia Pahang Al-Sultan Abdullah
building	UMPSA Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaysia Pahang Al-Sultan Abdullah
content_source	UMPSA Institutional Repository
url_provider	http://umpir.ump.edu.my/
language	English English
topic	QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General)
spellingShingle	QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) Mohd Faizal, Ab Razak Mohd Izham, Jaya Ernawan, Ferda Ahmad Firdaus, Zainal Abidin Nugroho, Fajar Agung Comparative analysis of machine learning classifiers for phishing detection
description	In recent years, communication over the Internet has become the most effective media for leveraging social interactions during the COVID-19 pandemic. Nevertheless, the rapid increase use of digital platforms has led to a significant growth of Phishing Attacks. Phishing attacks are one of the most common security issues in digital worlds that can affects both individual and organization in keeping their confidential information secure. Various modern approaches can be used to target an individual and trick them into leaking their sensitive information, which can later, purposely be used to harm the targeted victim or entire organization depending on the cybercriminal's intent and type of data leaked. This paper evaluates phishing detection by using Naïve Bayes, Simple Logistic, Random Forest, Ada Boost and MLP classifications. This study discusses the comparative analysis on the effectiveness of classification for detecting phishing attacks. The results indicated that the detection system trained with the Random Forest produce higher accuracy of 97.98% than another classifier method.
format	Conference or Workshop Item
author	Mohd Faizal, Ab Razak Mohd Izham, Jaya Ernawan, Ferda Ahmad Firdaus, Zainal Abidin Nugroho, Fajar Agung
author_facet	Mohd Faizal, Ab Razak Mohd Izham, Jaya Ernawan, Ferda Ahmad Firdaus, Zainal Abidin Nugroho, Fajar Agung
author_sort	Mohd Faizal, Ab Razak
title	Comparative analysis of machine learning classifiers for phishing detection
title_short	Comparative analysis of machine learning classifiers for phishing detection
title_full	Comparative analysis of machine learning classifiers for phishing detection
title_fullStr	Comparative analysis of machine learning classifiers for phishing detection
title_full_unstemmed	Comparative analysis of machine learning classifiers for phishing detection
title_sort	comparative analysis of machine learning classifiers for phishing detection
publisher	Institute of Electrical and Electronics Engineers Inc.
publishDate	2022
url	http://umpir.ump.edu.my/id/eprint/39337/1/Comparative%20analysis%20of%20machine%20learning%20classifiers%20for%20phishing%20detection.pdf http://umpir.ump.edu.my/id/eprint/39337/2/Comparative%20analysis%20of%20machine%20learning%20classifiers%20for%20phishing%20detection_ABS.pdf http://umpir.ump.edu.my/id/eprint/39337/ https://doi.org/10.1109/ICICoS56336.2022.9930531
_version_	1822923859320373248
score	13.235318

Comparative analysis of machine learning classifiers for phishing detection

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