Bi-LSTM and Conventional Classifiers for Email Spam Filtering

Nowadays, emails play a major role in everyone’s day-to-day life. The number of people using email is rapidly growing. Because of this, the hackers are taking advantage of the opportunity to use the emails as their secret weapons against the email users. The hackers send a bulk of emails at a time with a single click. The majority of spam mail contains advertisements or promotions for various events, utilities, appliances, and so on. A single Spam email results in a net loss for the user. Even with current technology, there are numerous techniques and methods for distinguishing between spam and ham mail, but spammers are finding ways to get the user's attention and developing themselves due to the decreased effectiveness of techniques. So, our study comes up with effective classifiers in this paper. In this research paper, ML and deep learning algorithms such as Naive Bayes Classifier, Random Forest, Artificial Neural Network, Support Vector Machine, Long Short-Term Memory, and Bidirectional-Long Short-Term Memory are used to identify which model is more accurate to classify the emails as spam or ham.