İstenmeyen E-postaların Tespiti için Kullanılan Yöntemlerin İncelenmesi
Spam e-mails are a method that is sent to the recipient without his consent and is generally used by people with malicious or promotional purposes. E-mails 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 spam to the e-mail accounts they never knew. In this study, the methods in the literature for filtering spam e-mails were examined. These spam filtering methods are mainly examined under two main headings: non-artificial intelligence-based and artificial intelligence-based. It is seen that non-artificial intelligence-based methods give effective results in detecting spam, but there are techniques in the literature that can bypass these methods. It is seen that the systems that use artificial intelligence-based machine learning algorithms in detecting spam have increased in popularity and research has gained momentum in this direction. Especially deep learning methods have been preferred for spam detection due to their high performance. In the literature, it is seen that it provides high performance in spam detection methods using algorithms such as Bayes, Support Vector Machine, Artificial Neural Network, Random Forest, Multilayer Perceptron, and K-Nearest Neighbour, which are classical machine learning methods. It has been demonstrated using different datasets that deep learning-based spam detection methods using Long Short Term Memory and Convolutional Neural Network algorithms further increase the performance rates. Besides, open problems found in spam detection systems and the stage of these studies in Turkish are also examined in this study and various suggestions have been made.
Eryılmaz, E. Enes and Kılıç, Erdal
"İstenmeyen E-postaların Tespiti için Kullanılan Yöntemlerin İncelenmesi,"
Dicle University Journal of Engineering: Vol. 11
, Article 9.
Available at: https://duje.dicle.edu.tr/journal/vol11/iss3/9