Yapay Zeka Teknikleri İle Gelen E-Postaların Ayrıştırılması

Year 2021, Issue: 21, 690 - 696, 31.01.2021

Mete Yağanoğlu , Erdal Irmak

https://doi.org/10.31590/ejosat.841299

Abstract

Teknolojik gelişmeler, bireyleri ve kuruluşları, iletişim kurmak ve bilgi paylaşmak için e-postalara daha bağımlı hale getirmektedir. E-postaların internet üzerinden önemli ve popüler bir iletişim olarak artan kullanımı, İnternet’i ve toplumu etkileyen ciddi bir tehdit oluşturmaktadır. Spam epostalar internet kullanıcıları için güvenlik sorunlarına sebep olmaktadır ve depolama, bant genişliği ve üretkenlik açısından kaynakları boşa harcamaktadır. İstenmeyen e-postaların hacmindeki artış, daha güvenilir ve sağlam antispam filtrelerin geliştirilmesi için yoğun bir ihtiyaç yaratmıştır. Bu nedenle, uyarlanabilir spam algılama modellerinin önerilmesi bir gereklilik haline gelmektedir. Bu çalışmada, spam e-postalarını başarılı bir şekilde tespit etmek ve filtrelemek için yapay zekaya dayalı akıllı bir algılama sistemi önerilmektedir Makine öğrenimi yöntemleri, mevcut verileri kullanarak en iyi modelleri oluşturmayı ve önceki veriler kullanılarak oluşturulan model yardımıyla yeni verileri en doğru şekilde analiz etmeyi amaçlamaktadır. Bu çalışmada, istenmeyen posta tespiti makine öğrenimi yöntemleri kullanılarak gerçekleştirilmiştir ve % 98,2 başarı oranına ulaşılmıştır.

Keywords

Spam Tespiti, Doğal Dil İşleme, Yapay Zeka, Makine Öğrenmesi

Separation of Incoming E-Mails Through Artificial Intelligence Techniques

Abstract

Technological developments are making individuals and organizations ever more dependent on e-mail to communicate and share information. Increasing use of e-mail as an important and popular method of communication poses potentially serious threats to the Internet and society. Spam e-mails cause security problems for internet users, and waste storage, bandwidth and productivity resources. The increase in the volume of spam e-mails has created an intense need for the development of more reliable and robust antispam filters. Therefore, it has become necessary to recommend adaptive spam detection models. In this paper, an intelligent system for the detection and filtering of spam e-mails is described. Machine learning methods aim to create the best models using the available data, and to analyze new data in the most accurate way, with the help of the model created using previous data. In this study, spam detection was carried out using machine learning methods. The classification achieved a success rate of 98,2% in spam detection.

Keywords

Spam Detection, Natural Language Processing, Artificial Intelligence, Machine Learning

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