Comparative performance of machine learning methods for classification on phishing attack detection

Siti Noranisah, Wan Ahmad and Mohd Arfian, Ismail and Edi, Sutoyo and Shahreen, Kasim and Mohd Saberi, Mohamad (2020) Comparative performance of machine learning methods for classification on phishing attack detection. International Journal of Advanced Trends in Computer Science and Engineering, 9 (1 SI-5). pp. 349-354. ISSN 2278-3091. (Published)

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The development of computer networks today has increased rapidly. This can be shown based on the trend of every computer user around the world, whereby they need to connect their computer to the Internet. This indicates that the use of Internet is very important, such as for the access to social media accounts, namely Instagram, Facebook, and Twitter. However, with this extensive use, the Internet does not necessarily have the ability to maintain account security in mobile phones or computers. With a low level of security in a network system, it will be convenient for scammers to hack a victim’s computer system and retrieve all important information of the victim for their benefit There are many methods that used by scammers to get the important information where phishing attack is the simplest and famous method to be used. Therefore, this study was conducted to develop an anti-phishing method to detect the phishing attack. Machine learning method was proposed as suitable to be used in detecting phishing attacks. In this paper, several machine learning methods were studied and applied in detecting phishing attack. Experiments of the machine learning methods were conducted to investigate which method performed better. Two benchmark datasets were used in the interest to access the ability of the methods in detecting the phishing attack. Then the results were obtained to show the performance of each methods on all dataset.

Item Type: Article
Additional Information: Indexed by Scopus
Uncontrolled Keywords: Machine learning; Phishing; Classification
Subjects: Q Science > QA Mathematics > QA76 Computer software
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Faculty/Division: Institute of Postgraduate Studies
Faculty of Computing
Depositing User: Mrs Norsaini Abdul Samat
Date Deposited: 18 Aug 2022 06:57
Last Modified: 18 Aug 2022 06:57
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