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Phishing URL Attack Detection using Logistic Regression and Convolutional Neural Network
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 1 |
| Year of Publication: 2025 |
| Authors: Umejuru Daniel, Eke Bartholomew, Fubara Egbono |
10.5120/ijca2025924611
|
Umejuru Daniel, Eke Bartholomew, Fubara Egbono . Phishing URL Attack Detection using Logistic Regression and Convolutional Neural Network. International Journal of Computer Applications. 187, 1 ( May 2025), 8-14. DOI=10.5120/ijca2025924611
Abstract
Phishing is a problem that is quickly spreading worldwide and costs internet users billions of dollars each year. It is illegal to gather sensitive information from internet users using social engineering techniques combined with technology. The overall performance is unreliable, inefficient, and requiring improvement in terms of prediction accuracy, time complexity, misclassification error and robustness. Phishing strategies can be recognized in a variety of communication channels, including email, instant chats, pop-up messages, and web pages itself. Over time, existing methods and approaches have been unable to detect all connected dangers and provide an all-encompassing solution. Although it is widely believed that a successful phishing attack entails developing a website that looks exactly like the target site in order to trick the internet user, this idea has not been incorporated into existing approaches to assess the dangers and thoroughly analyze the gaps. In this study, the aim is to create an enhanced Phishing attack detection system utilizing logistic regression and Convolutional Neural Network (CNN). This study outlined a novel method capable of identifying malicious phishing URLs with an emphasis on using features primarily obtained from the phishing and real URL addresses. The model kernel, weights, and bias values were tuned with a penalty term which increased model detection accuracy. The experimental findings show that CNN performed better incorporating penalty term which recorded a detection accuracy of 98.20% and LR yielded a recommendable prediction accuracy of 89.85%.
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