An Ensemble Approach for Detection of Malicious URLs Using SOM and Tabu Search Optimization
The fast spread of malicious URLs is a significant risk to online safety, since it makes assaults like spam, phishing, virus distribution, and vandalism of websites easier to carry out. The dynamic nature of these threats makes traditional detection techniques unable to keep up. Enhanced methods tha...
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| Format: | Article |
| Language: | English |
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Erbil Polytechnic University
2025-07-01
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| Series: | Polytechnic Journal |
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| Online Access: | https://polytechnic-journal.epu.edu.iq/home/vol15/iss2/2 |
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| author | Simar Preet Singh Abhilash Maroju Mohammad Kamrul Hasan Karan Tejpal |
| author_facet | Simar Preet Singh Abhilash Maroju Mohammad Kamrul Hasan Karan Tejpal |
| author_sort | Simar Preet Singh |
| collection | DOAJ |
| description | The fast spread of malicious URLs is a significant risk to online safety, since it makes assaults like spam, phishing, virus distribution, and vandalism of websites easier to carry out. The dynamic nature of these threats makes traditional detection techniques unable to keep up. Enhanced methods that can handle large-scale datasets and identify new attack patterns are needed for the real-time identification of malicious URLs. This research presents a strong hybrid machine learning approach that combines accurate classification with effective feature extraction. For feature extraction, we provide a Self-Organizing Map based Radial Movement Optimization (SOM-RMO); for classification, we present an Ensemble Radial Basis Function Network (ERBFN) optimized by Tabu Search. An RBFN tuned via Tabu Search guarantees high accuracy in the harmful URL categorization; meanwhile, the SOM-RMO efficiently performs dimensionality reduction, accentuating vital features. Our model performs better than other models in a variety of malicious URL attack scenarios. It substantially outperformed conventional detection techniques, attaining an accuracy of 97.1 %, precision of 96.4 %, recall of 95.8 %, and an F1-score of 96.0 % on a benchmark dataset. |
| format | Article |
| id | doaj-art-03bae8c1bffb44f58cf7ba9a29d3919c |
| institution | Kabale University |
| issn | 2707-7799 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Erbil Polytechnic University |
| record_format | Article |
| series | Polytechnic Journal |
| spelling | doaj-art-03bae8c1bffb44f58cf7ba9a29d3919c2025-08-21T07:55:16ZengErbil Polytechnic UniversityPolytechnic Journal2707-77992025-07-01152128136https://doi.org/10.59341/2707-7799.1852An Ensemble Approach for Detection of Malicious URLs Using SOM and Tabu Search OptimizationSimar Preet Singh0Abhilash Maroju1Mohammad Kamrul Hasan2Karan Tejpal3Bennett University, Greater Noida, IndiaDepartment of Information Technology, University of the Cumberlands, USAFaculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Selangor, MalaysiaUniversity of Massachusetts, Lowell, USAThe fast spread of malicious URLs is a significant risk to online safety, since it makes assaults like spam, phishing, virus distribution, and vandalism of websites easier to carry out. The dynamic nature of these threats makes traditional detection techniques unable to keep up. Enhanced methods that can handle large-scale datasets and identify new attack patterns are needed for the real-time identification of malicious URLs. This research presents a strong hybrid machine learning approach that combines accurate classification with effective feature extraction. For feature extraction, we provide a Self-Organizing Map based Radial Movement Optimization (SOM-RMO); for classification, we present an Ensemble Radial Basis Function Network (ERBFN) optimized by Tabu Search. An RBFN tuned via Tabu Search guarantees high accuracy in the harmful URL categorization; meanwhile, the SOM-RMO efficiently performs dimensionality reduction, accentuating vital features. Our model performs better than other models in a variety of malicious URL attack scenarios. It substantially outperformed conventional detection techniques, attaining an accuracy of 97.1 %, precision of 96.4 %, recall of 95.8 %, and an F1-score of 96.0 % on a benchmark dataset.https://polytechnic-journal.epu.edu.iq/home/vol15/iss2/2malicious urls,machine learning,feature extraction,tabu search,radial basis function network (rbfn) |
| spellingShingle | Simar Preet Singh Abhilash Maroju Mohammad Kamrul Hasan Karan Tejpal An Ensemble Approach for Detection of Malicious URLs Using SOM and Tabu Search Optimization Polytechnic Journal malicious urls, machine learning, feature extraction, tabu search, radial basis function network (rbfn) |
| title | An Ensemble Approach for Detection of Malicious URLs Using SOM and Tabu Search Optimization |
| title_full | An Ensemble Approach for Detection of Malicious URLs Using SOM and Tabu Search Optimization |
| title_fullStr | An Ensemble Approach for Detection of Malicious URLs Using SOM and Tabu Search Optimization |
| title_full_unstemmed | An Ensemble Approach for Detection of Malicious URLs Using SOM and Tabu Search Optimization |
| title_short | An Ensemble Approach for Detection of Malicious URLs Using SOM and Tabu Search Optimization |
| title_sort | ensemble approach for detection of malicious urls using som and tabu search optimization |
| topic | malicious urls, machine learning, feature extraction, tabu search, radial basis function network (rbfn) |
| url | https://polytechnic-journal.epu.edu.iq/home/vol15/iss2/2 |
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