A Comprehensive Review of Machine and Deep Learning Approaches for Cyber Security Phishing Email Detection
Yazarlar (2)
Sarmad Rashed
Doç. Dr. Caner ÖZCAN Karabük Üniversitesi, Türkiye
| Makale Türü |
Diğer (Teknik, not, yorum, vaka takdimi, editöre mektup, özet, kitap krıtiği,
araştırma notu, bilirkişi raporu ve benzeri)
(Diğer hakemli uluslararası dergilerde yayınlanan teknik not,
editöre mektup, tartışma, vaka takdimi ve özet türünden makale)
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| Dergi Adı | Al-Iraqia Journal for Scientific Engineering Research | ||
| Dergi ISSN | 2710-2165 | ||
| Makale Dili | İngilizce | Basım Tarihi | 01-2024 |
| Cilt / Sayı / Sayfa | 3 / 3 / 1–12 | DOI | 10.58564/IJSER.3.3.2024.219 |
| Makale Linki | https://ijser.aliraqia.edu.iq/index.php/ijser/article/view/219/97 | ||
| UAK Araştırma Alanları |
Görüntü İşleme
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| Özet |
| Over the past fifteen years, phishing has emerged as the leading cybercriminal activity, resulting in the unauthorized acquisition of substantial financial resources amounting to billions of dollars. This phenomenon arises due to using novel (zero-day) and complicated tactics by phishing attackers to deceive internet users. Email is the primary approach utilized to initiate phishing attacks. This study comprehensively analyzes popular methods used in email spam tests. The present analysis comprehensively examines the key concepts, techniques, and research trends relative to spam filtering. The topic of discussion involved a general email spam filtering mechanism and the attempts of various scholars to counter spam by employing machine-learning methodologies. Our review examines the advantages and disadvantages of several machine learning methods within the context of spam filtering while addressing some of the biggest research inquiries in this domain. |
| Anahtar Kelimeler |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Google Scholar | 6 |