Politeknik Dergisi 2147-9429 Gazi Üniversitesi 10.2339/politeknik.1745083 Deep Learning Neural Networks Derin Öğrenme Nöral Ağlar E-posta Kimlik Avı Sınıflandırması için Derin Öğrenme ve Açıklanabilir Yapay Zekâ: TabNet, NODE ve FT-Transformer Modellerinin Karşılaştırmalı Bir İncelemesi Deep Learning and Explainable AI for Email Phishing Classification: A Comparative Study of TabNet, NODE and FT-Transformer Models https://orcid.org/0009-0003-3729-8170 Asal Burçak ADANA BİLİM VE TEKNOLOJİ ÜNİVERSİTESİ https://orcid.org/0000-0003-3880-3039 Oyucu Saadin GAZİ ÜNİVERSİTESİ https://orcid.org/0000-0002-9203-697X Doğan Ferdi ADIYAMAN ÜNİVERSİTESİ https://orcid.org/0000-0001-9313-4910 Polat Onur BİNGÖL ÜNİVERSİTESİ https://orcid.org/0000-0002-2563-1218 Aksöz Ahmet Kayseri University 03 29 2026 29 3 1 13 07 17 2025 09 29 2025 Copyright © 1998, Politeknik Dergisi 1998 Politeknik Dergisi

Siber güvenlik tehditlerinin değişen yapısında, kimlik avı e-postaları sürekli ve yıkıcı bir saldırı vektörü olmaya devam etmektedir. Bu çalışma, tablosal veriler kullanılarak gerçekleştirilen bir kimlik avı e-posta sınıflandırma görevinde, TabNet, NODE (Neural Oblivious Decision Ensembles) ve FT-Transformer mimarilerine odaklanarak derin öğrenme modellerinin etkinliğini araştırmaktadır. Kullanılan veri kümesi, e-postaların dilsel ve yapısal özelliklerini yansıtan sekiz giriş özelliği ile birlikte kimlik avı ya da normal sınıflandırmasını belirten ikili bir etiketi içermektedir. Ayrıca, veri kümesinde gözlenen ciddi sınıf dengesizliği problemiyle başa çıkmak amacıyla NearMiss alt örnekleme yaklaşımı uygulanmıştır. Deneysel sonuçlar, her üç modelin de güçlü performanslar sergilediğini ancak FT-Transformer modelinin en yüksek doğruluk (accuracy) ve dengeli hassasiyet-duyarlılık (precision-recall) skorlarıyla TabNet ve NODE modellerinden daha iyi performans gösterdiğini ortaya koymaktadır. Ayrıca, FT-Transformer modelinin karar verme sürecini yorumlamak için SHAP ve LIME gibi açıklanabilir yapay zekâ (XAI) yöntemleri kullanılmıştır ki bu karar verme süreci yazım hataları, eşsiz kelime sayıları ve aciliyetle ilişkili anahtar kelimelerin kimlik avı tespitinde kritik bir rol oynadığı vurgulamaktadır. Elde edilen bulgular, siber güvenlik alanındaki sekmeli veriler üzerinde transformer tabanlı yaklaşımların potansiyelini vurgulamakta ve kimlik avı tespit sistemlerinde güven ve şeffaflığı artırmak adına açıklanabilir yapay zekânın önemini ortaya koymaktadır.

In the changing landscape of cybersecurity threats, phishing emails indicate a persistent and damaging attack vector. This study investigates the effectiveness of deep learning models on a phishing email classification task using tabular data and focusing on TabNet, NODE (Neural Oblivious Decision Ensembles), and FT-Transformer architectures. The utilized dataset includes eight input features capturing linguistic and structural characteristics of emails, with a binary label indicating phishing or normal classification. Additionally, the NearMiss under-sampling approach is applied to address the significant class imbalance. Experimental results demonstrate that while all three models achieve strong performance, the FT-Transformer model outperforms TabNet and NODE by achieving the highest classification accuracy and balanced precision-recall scores. Additionally, explainable artificial intelligence (XAI) methods, SHAP and LIME, are employed to interpret the FT-Transformer model’s decision-making process, which highlights the critical role of spelling errors, unique word counts, and urgency-related keywords in phishing detection. The findings emphasize the potential of transformer-based approaches for tabular cybersecurity applications and indicate the importance of interpretable AI in enhancing trust and transparency in phishing detection systems.

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