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URL Phishing Detection Using Particle Swarm Optimization and Data Mining

Saeed M. Alshahrani1, Nayyar Ahmed Khan1,*, Jameel Almalki2, Waleed Al Shehri2

1 College of Computing and IT-Shaqra University, Shaqra, 155572, Saudi Arabia
2 Department of Computer Science, College of Computer in Al-Leith, Umm Al-Qura University, Makkah, Saudi Arabia

* Corresponding Author: Nayyar Ahmed Khan. Email:

Computers, Materials & Continua 2022, 73(3), 5625-5640.


The continuous destruction and frauds prevailing due to phishing URLs make it an indispensable area for research. Various techniques are adopted in the detection process, including neural networks, machine learning, or hybrid techniques. A novel detection model is proposed that uses data mining with the Particle Swarm Optimization technique (PSO) to increase and empower the method of detecting phishing URLs. Feature selection based on various techniques to identify the phishing candidates from the URL is conducted. In this approach, the features mined from the URL are extracted using data mining rules. The features are selected on the basis of URL structure. The classification of these features identified by the data mining rules is done using PSO techniques. The selection of features with PSO optimization makes it possible to identify phishing URLs. Using a large number of rule identifiers, the true positive rate for the identification of phishing URLs is maximized in this approach. The experiments show that feature selection using data mining and particle swarm optimization helps tremendously identify the phishing URLs based on the structure of the URL itself. Moreover, it can minimize processing time for identifying the phishing website instead. So, the approach can be beneficial to identify such URLs over the existing contemporary detecting models proposed before.


Cite This Article

S. M. Alshahrani, N. A. Khan, J. Almalki and W. Al Shehri, "Url phishing detection using particle swarm optimization and data mining," Computers, Materials & Continua, vol. 73, no.3, pp. 5625–5640, 2022.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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