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 (Published)
Machine Learning Technique for Phishing Website Detection.pdf
Restricted to Repository staff only
Download (1MB) |
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: | https://umpir.ump.edu.my/id/eprint/40812 |
| Statistic Details: | View Download Statistic |

