Botnet Spam E-Mail Detection Using Deep Recurrent Neural Network

The significant amount of SPAM emails that are derived from various botnets worldwide affect the limited capacity of mailboxes. They affect the security of personal mail and the space-loss from the communication. They affect the time required for identifying spam emails and addressing them. Till today, the email spam detection is still considered a challenging process. That is because the email spam is still happening a lot. It is because the detection still needs much improvement. Therefore, the researcher of this study develops a Gated Recurrent Unit Recurrent Neural Network (GRU-RNN) with SVM for Bot Spam email detection. The developed approach got tested by employing the Spambase dataset. The approach shows an accuracy of 98.7%. Through conducting extensive experiments, the researcher concludes that the proposed approach shows an excellent capability of detecting spam email.