Machine learning technique for phishing website detection

Nurul Amira, Mohd Zin and Mohd Faizal, Ab Razak and Ahmad Firdaus, Zainal Abidin and Ernawan, Ferda and Nor Saradatul Akmar, Zulkifli Machine learning technique for phishing website detection. In: 2023 IEEE 8th International Conference On Software Engineering and Computer Systems (ICSECS) , 25-27 August 2023 , Penang, Malaysia. . ISBN 979-8-3503-1093-1

	Pdf Machine Learning Technique for Phishing Website Detection.pdf Restricted to Repository staff only Download (1MB) | Request a copy
Preview	Pdf machine learning technique for phishing website.pdf Download (1MB) | Preview

Abstract

The Internet has emerged as an indispensable tool in both our personal and professional life in our modern day. As a direct consequence of this, the number of customers who make their purchases over the Internet is quickly increasing. Internet users may be vulnerable to a wide variety of web threats because of this fact. These threats may result in monetary loss, fraudulent use of credit cards, loss of personal data, potential damage to a brand's reputation, and customer mistrust in e-commerce and online banking. Phishing is a sort of cyber threat that may be defined as the practice of imitating a genuine website for the purpose of stealing sensitive information such as usernames, passwords, and credit card numbers. This research focuses on strategies for detecting phishing attacks. This study apply a machine learning approach to detect a phishing attack. As a result, this study able to detect phishing with accuracy 94%.

Item Type:	Conference or Workshop Item (Lecture)
Uncontrolled Keywords:	Phishing attack; Phishing; Website detection; Malware; Machine learning
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty/Division:	Institute of Postgraduate Studies Faculty of Computing
Depositing User:	Mrs Norsaini Abdul Samat
Date Deposited:	01 Apr 2024 04:27
Last Modified:	01 Apr 2024 04:27
URI:	http://umpir.ump.edu.my/id/eprint/40812
Download Statistic:	View Download Statistics

Actions (login required)

View Item