The increase in the creation of fake web pages by attackers is leading to a sharp increase in cyberattacks. Attackers use these fake web sites to advertise products to Internet users, distribute malicious programs, or steal users' valuable logins and passwords. Traditional solutions for detecting such fake web addresses are not effective in detecting newly created fake web addresses. In this article, we propose a new approach that combines several machine learning algorithms. It can be seen that the Random Forest classifier (accuracy of 99%) can be considered more reliable than the others in detecting fake web addresses.
| Mualliflar | Кабулов, Анвар, Отахонов, Алишербек |
|---|---|
| Jurnal | Al-Farg'oniy avlodlari |
| Nashr sanasi | 2025-10-08 |
| Son | 3 |
| Betlar | 174-181 |
| Til | Rus |
веб-безопасность, машинное обучение, Random Forest, кибератаки, поддельные веб-сайты, URL, web Security, Machine Learning, Random Forest, Cyberattacks, Fake Websites, URL, veb xavfsizligi, Mashinani o'rganish, Tasodifiy o'rmon, kiberhujumlar, Soxta veb-saytlar, URL
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