Türkçe E-postalarda Spam Tespiti için Makine Öğrenme Yöntemlerinin ve Dil Modellerinin Analizi

Öz

Son zamanlarda teknolojinin ve sosyal ağların gelişmesiyle çevrimiçi karşılıklı etkileşim, herhangi konuda fikirlerini paylaşma oldukça önem kazanmıştır. Bu etkileşimlerin olumlu yanı olsa da olumsuz yanı da oldukça fazladır. Sosyal ağlarda kullanıcıların bilgilerini elde edip kullanıcıları taklit etmek güvenlik açısından büyük bir problemdir. Böylelikle kullanıcılar üzerinden dolandırıcılık vs. yapılabilmektedir. Kullanıcıları taklit edebilmek için en yaygın yol spam mesajların, e-postaların, vs. atılmasıdır. Güvenlik probleminin üstesinden gelmek için spam filtreleme, spam tespiti yöntemi geliştirme gibi işlemler uygulanmaktadır. Bu çalışmada Türkçe e-postalarda spam içeren e-postaların tespiti için Rastgele Orman, Lojistik Regresyon, Naive Bayes, Yapay Sinir Ağları makine öğrenme yöntemleri ve BERT, ELECTRA, ALBERT, DistilBERT dil modelleri analiz edilmiştir. Böylece dil modellerinin Türkçe için spam e-postaları sınıflandırmadaki etkisi gösterilmek istenmiştir. Deneysel çalışmaların sonucunda, spam e-postaları sınıflandırmada tüm dil modelleri makine öğrenme yöntemlerine göre daha başarılı olmuştur. Makine öğrenme yöntemlerinden yapay sinir ağları %90.15 doğrulu değeri elde ederken, en başarılı dil modelleri %94.08 doğruluk değeri ile BERT ve ELECTRA olmuştur.

Anahtar Kelimeler

• Siber Güvenlik
• Spam Tespiti
• Dil Modeli
• Makine Öğrenmesi
• Doğal Dil İşleme
• Metin Sınıflandırma
• Cyber Security
• Spam Detection
• Language Model
• Machine Learning
• Natural Language Processing
• Text Classification

Analysis of Machine Learning Methods and Language Models for Spam Detection in Turkish Emails

Abstract

Recently, with the development of technology and social networks, online interaction, sharing ideas on any subject has gained importance. While there are positive aspects to these interactions, there are also many negative aspects. Obtaining users' information and impersonating users in social networks is a big problem in terms of security. Thus, fraud etc. can be done by under cover of users. The most common way to impersonate users is by sending spam messages, emails, etc. In order to overcome the security problem, processes such as spam filtering and spam detection method development are applied. In this study, Random Forest, Logistic Regression, Naive Bayes, Artificial Neural Networks machine learning methods and BERT, ELECTRA, ALBERT, DistilBERT language models were analyzed to detect e-mails containing spam in Turkish e-mails. Thus, it is aimed to show the effect of language models in classifying spam e-mails for Turkish. As a result of experimental studies, all language models were more successful than machine learning methods in classifying spam emails. While artificial neural networks from machine learning methods achieved 90.15% accuracy, the most successful language models were BERT and ELECTRA with 94.08% accuracy.

Keywords

• Siber Güvenlik
• Spam Tespiti
• Dil Modeli
• Makine Öğrenmesi
• Doğal Dil İşleme
• Metin Sınıflandırma
• Cyber Security
• Spam Detection
• Language Model
• Machine Learning
• Natural Language Processing
• Text Classification

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