A cyber defense system against phishing attacks with deep learning game theory and LSTM-CNN with African vulture optimization algorithm (AVOA)
Yazarlar (4)
Mustafa Ahmed Elberri
University Of Kastamonua, Türkiye
University Of Kastamonua, Türkiye
Dr. Öğr. Üyesi Ümit TOKEŞER Kastamonu Üniversitesi, Türkiye
Javad Rahebı
Istanbul Topkapi University, Türkiye
Istanbul Topkapi University, Türkiye
Jose Manuel Lopez-Guede
Universidad Del Pais Vasco, İspanya
Universidad Del Pais Vasco, İspanya
| Makale Türü |
Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
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| Dergi Adı | International Journal of Information Security (Q2) | ||
| Dergi ISSN | 1615-5262 Dergi Bilgileri (2024) | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | Türkçe | Basım Tarihi | 05-2024 |
| Kabul Tarihi | 08-04-2024 | Yayınlanma Tarihi | 05-05-2024 |
| Cilt / Sayı / Sayfa | 23 / 4 / 2583–2606 | DOI | 10.1007/s10207-024-00851-x |
| Makale Linki | https://doi.org/10.1007/s10207-024-00851-x | ||
| UAK Araştırma Alanları |
Geometri
Cebir ve Sayılar Teorisi
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| Özet |
| Phishing attacks pose a significant threat to online security, utilizing fake websites to steal sensitive user information. Deep learning techniques, particularly convolutional neural networks (CNNs), have emerged as promising tools for detecting phishing attacks. However, traditional CNN-based image classification methods face limitations in effec... |
| Anahtar Kelimeler |
| African vulture optimization algorithm (AVOA) | Convolutional neural networks | Deep learning | Fake pages | Feature selection | Game theory | LSTM | Phishing attacks | SMOTE |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 14 |
| Scopus | 27 |