İstenmeyen Epostaların Tespiti için Kullanılan Yöntemlerin İncelenmesi

Abstract

In this study, the methods in the literature for filtering spam emails were reviewed. Emails are actively used by people or communities who want to make propaganda, advertising, phishing because of their ease of use and low cost. People or communities who want to achieve their goals send unnecessary and unsolicited mail to the email accounts they never knew. These mails cause serious financial and moral damages to internet users and also engage in internet traffic. Unsolicited emails are a method that is sent to the recipient without his consent and that is generally used by malicious or promotional purposes. In this article, important developments in spam filtering methods are evaluated and deficiencies are revealed. The filtering of spam emails has been reviewed under two main headings: non-artificial intelligence-based and artificial intelligence-based. It has been observed that non-artificial intelligence-based methods give effective results in detecting spam, but there is spam that can easily skip these methods. It has been revealed that systems based on artificial intelligence are frequently used in spam detection and research and development are in this direction. In recent years, with the development of artificial intelligence, machine learning and algorithms emerging in its deep learning field, which is a sub-branch of it, have been detected with high performance and spam email detection. Due to the high performance of machine learning and deep learning methods for filtering spam emails, studies in this field are concentrated in detecting and filtering spam email.