nowadays, several algorithms based on machine learning are presented to detect phishing attempts. However, these approaches often suffer from low accuracy, as well as long response times and high false positive rates that reduce the effectiveness of these algorithms. In addition, most of the existing methods rely on a predefined set of features, which may limit their flexibility and robustness. In future research, advanced techniques such as machine learning and deep learning to study and identify changing threats will help identify phishing indicators. This approach will improve the overall effectiveness of cybersecurity measures against phishing attacks
| Mualliflar | Otaxonov, Alisherbek |
|---|---|
| Jurnal | Al-Farg'oniy avlodlari |
| Nashr sanasi | 2024-12-26 |
| Son | 4 |
| Betlar | 397-401 |
| Til | Rus |
Фишинг, Фишинговые атаки, Унифицированный указатель ресурсов (URL), Машинное обучение, Взаимная информация, Случайный лес, Phishing, Phishing attacks, Uniform Resource Locator (URL), Machine learning, Mutual information, Random Forest, Fishing, Fishing hujumlari, Yagona Resurs Locator (URL), Mashinani o'rganish, O‘zaro ma’lumot, Tasodifiy o‘rmon
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