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

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)

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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.

Item Type: Article
Additional Information: Indexed by Scopus
Uncontrolled Keywords: Classification; Machine learning; Phishing
Subjects: H Social Sciences > HE Transportation and Communications
Q Science > QA Mathematics > QA76 Computer software
T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Faculty/Division: Faculty of Computer System And Software Engineering
Institute of Postgraduate Studies
Depositing User: Mrs Norsaini Abdul Samat
Date Deposited: 12 Mar 2020 09:12
Last Modified: 12 Mar 2020 09:12
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