Vol.10, No.2, May 2021.                                                                                                                                                                                    ISSN: 2217-8309

                                                                                                                                                                                                                         eISSN: 2217-8333


TEM Journal



Association for Information Communication Technology Education and Science

Detecting the Phishing Website with the Highest Accuracy


Hesham Abusaimeh, Yusra Alshareef


© 2021 Hesham Abusaimeh, published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. (CC BY-NC-ND 4.0)


Citation Information: TEM Journal. Volume 10, Issue 2, Pages 947-953, ISSN 2217-8309, DOI: 10.18421/TEM102-58, May 2021.


Received: 22 September 2020.

Revised:   22 April 2021.
Accepted: 29 April 2021.
Published: 27 May 2021.




Phishing attacks are increasing and it becomes necessary to use appropriate response methods and to respond effectively to phishing attacks. This paper aims to uncover phishing attack sites by analyzing a three-module set to prevent damage and reconsider the awareness of phishing attacks. Based on the analyzed content, a countermeasure was proposed for each type of phishing attack by using website features. These features will be classified in order to determine the effectiveness of the countermeasure. Finally, the proposed method enhanced the site security as anti-phishing technology. The phishing detection used three classification algorithms, which are the decision tree; the supporting vector machine and the random forest were combined into one system that was proposed in this paper for the purpose of obtaining the highest accuracy in detecting phishing sites. The results of the proposed algorithm showed 98.52% higher accuracy than others.


Keywords –Decision tree, Supporting vector machine, Random forest.



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