Optimizing malicious website prediction: An advanced XGBoost-based machine learning model
In the substantial area of the Internet, some websites can be quite harmful and troublesome for both individuals and businesses. Our methods for identifying and forecasting these malicious websites are not always reliable; they can be slow and inaccurate. What if you had technology that could alert...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
De Gruyter
2025-05-01
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| Series: | Nonlinear Engineering |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/nleng-2024-0069 |
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