Türkçe Oltalama E-Postalarının Anlamsal Tespiti: Doğal Dil İşleme ve Derin Öğrenme Tabanlı Bir Yaklaşım

Abstract

Bu çalışma, Türkçe e-posta içeriklerindeki kimlik avı (phishing) saldırılarını anlamsal düzeyde tespit etmeye yönelik bir metin sınıflandırma yaklaşımı sunmaktadır. Gerçek ve sahte e-postalardan dengeli olarak oluşturulmuş bir veri kümesi kullanılmıştır. Ön işleme sürecinde küçük harfe dönüştürme, noktalama işaretlerinin temizlenmesi ve TF-IDF tabanlı vektörleştirme uygulanmış; bağlamsal temsiller ise BERTurk modeli aracılığıyla elde edilmiştir. Sınıflandırma işlemi Naive Bayes, SVM, LSTM, ELM ve BERT algoritmalarıyla gerçekleştirilmiştir. Modeller Google Colab ortamında eğitilmiş ve doğruluk, F1 skoru ile ROC-AUC metrikleri üzerinden değerlendirilmiştir. Sonuçlar, BERT modelinin Türkçe phishing e-postalarındaki anlamsal farkları başarılı biçimde ayırt ettiğini ortaya koymaktadır. Bu çalışma, morfolojik açıdan zengin dillerde phishing tespiti konusunda literatürdeki boşluğu doldurmayı amaçlamakta ve gerçek zamanlı siber güvenlik sistemlerine entegre edilebilecek ölçeklenebilir bir model önermektedir. Elde edilen bulgular, düşük kaynaklı dillerde bağlamsal doğal dil işleme yöntemlerinin etkinliğini de ortaya koymaktadır.

Keywords

• Derin Öğrenme
• Doğal Dil İşleme
• Kimlik Avı
• Makine Öğrenmesi
• Türkçe E-posta

Supporting Institution

Bu araştırma herhangi bir dış fon desteği almamıştır.

Ethical Statement

Bu makale insan veya hayvan denekleriyle ilgili herhangi bir çalışma içermemektedir. Çalışmanın hazırlanma sürecinde bilimsel ve etik ilkelere uyulmuş; yararlanılan tüm kaynaklar kaynakça bölümünde belirtilmiştir.

Thanks

Bu çalışmanın yürütülmesi sürecinde katkılarını esirgemeyen danışman hocama ve destek sağlayan tüm aka-demik personele teşekkür ederim.

Semantic Detection of Turkish Phishing Emails: A Natural Language Processing and Deep Learning-Based Approach

Abstract

This study proposes a semantic-based text classification approach for detecting phishing attacks in Turkish email content. A balanced dataset consisting of legitimate and fraudulent emails was constructed. The preprocessing phase included case normalization, punctuation removal, and TF-IDF-based vectorization, while contextual embeddings were obtained using the BERTurk model. Classification was performed using Naive Bayes, SVM, LSTM, ELM, and BERT algorithms. All models were trained in the Google Colab environment, and their performance was assessed using accuracy, F1-score, and ROC-AUC metrics. Results indicate that the BERT model provides superior performance in identifying semantic differences in Turkish phishing emails. The study addresses the gap in phishing detection for morphologically rich languages such as Turkish and presents a scalable model suitable for integration into real-time cybersecurity systems. The findings also demonstrate the viability of contextual NLP techniques in resource-scarce language environments.

Keywords

• Deep Learning
• Machine Learning
• Natural Language Processing
• Phishing
• Turkish Emails

Supporting Institution

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Ethical Statement

This article does not involve any studies with human or animal participants. Scientific and ethical principles were followed throughout the preparation of this study, and all sources used have been duly cited in the refe-rences section.

Thanks

I would like to express my sincere gratitude to my academic advisor and all supporting academic staff for their valuable contributions throughout the conduct of this study.

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