Hybrid Approach with a focus on Preprocessing Techniques for Detecting Phishing Websites
DOI:
https://doi.org/10.70715/jitcai.2025.v2.i2.023Keywords:
Cybersecurity, phishing detection, hybrid, approach, URL, analysis, Deep, LearningAbstract
Phishing is regarded as a significant cybersecurity problem in the digital era, utilizing the fabrication of fraudulent websites to deceive users and expropriate their sensitive information, including passwords and financial data. The growing dependence on the internet has led to a marked increase in the frequency of these attacks, resulting in considerable financial losses for individuals and businesses alike. This underscores the pressing necessity for efficient strategies to counteract such assaults.
This research aims to create a hybrid model for identifying phishing websites via URL analysis. The suggested model combines Convolutional Neural Networks (CNN) with Long Short-Term Memory networks (LSTM) and an Attention Mechanism to make predictions more accurate and uncover hidden patterns in the data. The model was trained on the "Url_Detection_Dataset" from the Kaggle platform, and its performance was assessed using precision, recall, and F1-score measures. The results showed that the hybrid model is better than traditional methods at telling apart real and harmful URLs, making it a useful tool in cybersecurity. The results provide a framework for subsequent research and promote the creation of more resilient, flexible, and effective solutions.
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Data Availability Statement
The datasets used in this study are publicly available on Kaggle (Url_Detection_Dataset). The processed data and model code can be provided by the author upon request.
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Copyright (c) 2025 Abeer Yahya Salawi (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
