Review of the machine learning methods in the classification of phishing attack

The development of computer networks today has increased rapidly. This can be seen based on the trend of computer users around the world, whereby they need to connect their computer to the Internet. This shows that the use of Internet networks is very important, whether for work purposes or access t...

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Main Authors: Jupin, John Arthur, Sutikno, Tole, Mohd Arfian, Ismail, Mohd Saberi, Mohamad, Shahreen, Kasim, Deris, Stiawan
Format: Article
Language:English
Published: IAES 2019
Subjects:
Online Access:http://umpir.ump.edu.my/id/eprint/26794/1/Review%20of%20the%20machine%20learning%20methods%20in%20the%20classification%20.pdf
http://umpir.ump.edu.my/id/eprint/26794/
https://doi.org/10.11591/eei.v8i4.1344
https://doi.org/10.11591/eei.v8i4.1344
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spelling my.ump.umpir.267942020-03-12T09:12:36Z http://umpir.ump.edu.my/id/eprint/26794/ Review of the machine learning methods in the classification of phishing attack Jupin, John Arthur Sutikno, Tole Mohd Arfian, Ismail Mohd Saberi, Mohamad Shahreen, Kasim Deris, Stiawan HE Transportation and Communications QA76 Computer software TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering The development of computer networks today has increased rapidly. This can be seen based on the trend of computer users around the world, whereby they need to connect their computer to the Internet. This shows that the use of Internet networks is very important, whether for work purposes or access to social media accounts. However, in widely using this computer network, the privacy of computer users is in danger, especially for computer users who do not install security systems in their computer. This problem will allow hackers to hack and commit network attacks. This is very dangerous, especially for Internet users because hackers can steal confidential information such as bank login account or social media login account. The attacks that can be made include phishing attacks. The goal of this study is to review the types of phishing attacks and current methods used in preventing them. Based on the literature, the machine learning method is widely used to prevent phishing attacks. There are several algorithms that can be used in the machine learning method to prevent these attacks. This study focused on an algorithm that was thoroughly made and the methods in implementing this algorithm are discussed in detail. IAES 2019 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/26794/1/Review%20of%20the%20machine%20learning%20methods%20in%20the%20classification%20.pdf Jupin, John Arthur and Sutikno, Tole and Mohd Arfian, Ismail and Mohd Saberi, Mohamad and Shahreen, Kasim and Deris, Stiawan (2019) Review of the machine learning methods in the classification of phishing attack. Bulletin of Electrical Engineering and Informatics, 8 (4). pp. 1545-1555. ISSN 2089-3191 (Print); 2302-9285 (Online) https://doi.org/10.11591/eei.v8i4.1344 https://doi.org/10.11591/eei.v8i4.1344
institution Universiti Malaysia Pahang
building UMP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang
content_source UMP Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic HE Transportation and Communications
QA76 Computer software
TA Engineering (General). Civil engineering (General)
TK Electrical engineering. Electronics Nuclear engineering
spellingShingle HE Transportation and Communications
QA76 Computer software
TA Engineering (General). Civil engineering (General)
TK Electrical engineering. Electronics Nuclear engineering
Jupin, John Arthur
Sutikno, Tole
Mohd Arfian, Ismail
Mohd Saberi, Mohamad
Shahreen, Kasim
Deris, Stiawan
Review of the machine learning methods in the classification of phishing attack
description The development of computer networks today has increased rapidly. This can be seen based on the trend of computer users around the world, whereby they need to connect their computer to the Internet. This shows that the use of Internet networks is very important, whether for work purposes or access to social media accounts. However, in widely using this computer network, the privacy of computer users is in danger, especially for computer users who do not install security systems in their computer. This problem will allow hackers to hack and commit network attacks. This is very dangerous, especially for Internet users because hackers can steal confidential information such as bank login account or social media login account. The attacks that can be made include phishing attacks. The goal of this study is to review the types of phishing attacks and current methods used in preventing them. Based on the literature, the machine learning method is widely used to prevent phishing attacks. There are several algorithms that can be used in the machine learning method to prevent these attacks. This study focused on an algorithm that was thoroughly made and the methods in implementing this algorithm are discussed in detail.
format Article
author Jupin, John Arthur
Sutikno, Tole
Mohd Arfian, Ismail
Mohd Saberi, Mohamad
Shahreen, Kasim
Deris, Stiawan
author_facet Jupin, John Arthur
Sutikno, Tole
Mohd Arfian, Ismail
Mohd Saberi, Mohamad
Shahreen, Kasim
Deris, Stiawan
author_sort Jupin, John Arthur
title Review of the machine learning methods in the classification of phishing attack
title_short Review of the machine learning methods in the classification of phishing attack
title_full Review of the machine learning methods in the classification of phishing attack
title_fullStr Review of the machine learning methods in the classification of phishing attack
title_full_unstemmed Review of the machine learning methods in the classification of phishing attack
title_sort review of the machine learning methods in the classification of phishing attack
publisher IAES
publishDate 2019
url http://umpir.ump.edu.my/id/eprint/26794/1/Review%20of%20the%20machine%20learning%20methods%20in%20the%20classification%20.pdf
http://umpir.ump.edu.my/id/eprint/26794/
https://doi.org/10.11591/eei.v8i4.1344
https://doi.org/10.11591/eei.v8i4.1344
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score 13.154949