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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| Format: | Article |
| Language: | Arabic |
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Higher Commission for Scientific Research
2025-07-01
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| Series: | Syrian Journal for Science and Innovation |
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| Online Access: | https://journal.hcsr.gov.sy/archives/1584 |
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| author | Sira Astour Ahmad Hasan |
| author_facet | Sira Astour Ahmad Hasan |
| author_sort | Sira Astour |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-d1df199291c5427f9c462cec17ec03ba |
| institution | DOAJ |
| issn | 2959-8591 |
| language | Arabic |
| publishDate | 2025-07-01 |
| publisher | Higher Commission for Scientific Research |
| record_format | Article |
| series | Syrian Journal for Science and Innovation |
| spelling | doaj-art-d1df199291c5427f9c462cec17ec03ba2025-08-20T03:08:32ZaraHigher Commission for Scientific ResearchSyrian Journal for Science and Innovation2959-85912025-07-0132Detecting Malicious URLs Using Classification Algorithms in Machine Learning and Deep LearningSira Astour0Ahmad Hasan1Faculty of Information Technology and Communication Engineering, Arab International University _ Daraa _ Syria.Web Science Program, Syrian Virtual University _ Damascus _ SyriaDue 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.https://journal.hcsr.gov.sy/archives/1584uniform resource locator (urls)supervised machine learningdeep learningensemble learningcybersecurityclassification algorithmsbenign urls |
| spellingShingle | Sira Astour Ahmad Hasan Detecting Malicious URLs Using Classification Algorithms in Machine Learning and Deep Learning Syrian Journal for Science and Innovation uniform resource locator (urls) supervised machine learning deep learning ensemble learning cybersecurity classification algorithms benign urls |
| title | Detecting Malicious URLs Using Classification Algorithms in Machine Learning and Deep Learning |
| title_full | Detecting Malicious URLs Using Classification Algorithms in Machine Learning and Deep Learning |
| title_fullStr | Detecting Malicious URLs Using Classification Algorithms in Machine Learning and Deep Learning |
| title_full_unstemmed | Detecting Malicious URLs Using Classification Algorithms in Machine Learning and Deep Learning |
| title_short | Detecting Malicious URLs Using Classification Algorithms in Machine Learning and Deep Learning |
| title_sort | detecting malicious urls using classification algorithms in machine learning and deep learning |
| topic | uniform resource locator (urls) supervised machine learning deep learning ensemble learning cybersecurity classification algorithms benign urls |
| url | https://journal.hcsr.gov.sy/archives/1584 |
| work_keys_str_mv | AT siraastour detectingmaliciousurlsusingclassificationalgorithmsinmachinelearninganddeeplearning AT ahmadhasan detectingmaliciousurlsusingclassificationalgorithmsinmachinelearninganddeeplearning |