Detecting Malicious URLs Using Classification Algorithms in Machine Learning and Deep Learning
Due to the daily necessity of using links and websites and the high prevalence of malicious URLs, many security threats arise for Internet users and organizations. These threats can lead to data breaches and identity theft, and they can cause a complete system collapse. Traditional methods of detect...
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| Main Authors: | , |
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| Format: | Article |
| Language: | Arabic |
| Published: |
Higher Commission for Scientific Research
2025-07-01
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| Series: | Syrian Journal for Science and Innovation |
| Subjects: | |
| Online Access: | https://journal.hcsr.gov.sy/archives/1584 |
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| Summary: | Due to the daily necessity of using links and websites and the high prevalence of malicious URLs, many security threats arise for Internet users and organizations. These threats can lead to data breaches and identity theft, and they can cause a complete system collapse. Traditional methods of detecting malicious URLs are often insufficient and require advanced technologies. This study presents an improvement in the accuracy and speed of detecting malicious URLs through ensemble learning techniques, specifically Bagging (Bootstrap) and Stacking. Extensive experiments on a large, balanced dataset containing 491,530 URLs, equally distributed between benign and malicious, showed that ensemble learning models significantly outperform other algorithms. The Bagging classifier, which uses decision trees as the base classifier, achieved an accuracy of 99.01%, a training time of 23.84 seconds, and a prediction time of 0.86 seconds. The Stacking classifier, which uses AdaBoost, Random Forest, and XGBoost as base classifiers, also achieved similar results, although the training time increased to 199.6944 seconds due to the complexity of this model. In addition to the results, we obtained, which demonstrated the superiority of bagging and stacking models, we conducted a comprehensive comparison with other popular models, ranging from individual machine learning models such as k-Nearest Neighbors, to deep learning models such as feedforward neural networks, to ensemble learning models with various techniques such as boosting. These results highlight the promising potential of ensemble learning in strengthening cybersecurity measures and protecting users and businesses from malicious URL attacks. |
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| ISSN: | 2959-8591 |