Classification of Temporary and Real E-mail Addresses with Machine Learning Techniques

Temporary e-mail addresses are e-mail addresses that users can quickly create without signing up. These e-mail addresses are useful for privacy and to avoid spam. However, they also pose several serious cyber threats, including fraud, spam campaigns, and fake account creation In this study, a method utilizing natural language processing and machine learning techniques is proposed to classify real and temporary e-mail addresses. First, temporary and real e-mail addresses are analyzed, and features are developed to identify the differences between them. These features include lexical structures, broad contexts, and structural features of e-mail addresses. Various machine learning algorithms were then applied on the resulting feature set to differentiate e-mail addresses. The results were evaluated with K-fold cross-validation method and an accuracy rate of 96% was obtained. This success rate shows that the developed method can successfully distinguish between real and temporary e-mail addresses.