Detecting span in emails using advanced machine learning methods

Abstract

E-mail is one of the quickest and most professional ways to send messages from one location to another around the world; however, increased use of e-mail has increased to received messages in the mailbox, where the recipient receives a large number of messages, some of which cause significant and varied problems, such as the theft of the recipient's identity, the loss of vital information, and network damage. These communications are so harmful that the user has no way of avoiding them, especially when they come in a variety of forms, such as adverts and other types of messages. Spam is the term for these emails Filtering is used to delete these spam communications and prevent them from being viewed. This research intends to improve e-mail spam filtering by proposing a single objective evaluation algorithm issue that uses Deep Learning, Genetic Algorithms, and Bayes theorem-based classifiers to build the optimal model for accurately categorizing e-mail messages. Text cleaning and feature selection are used as the initial stage in the modeling process to minimize the dimension of sparse text features obtained from spam and ham communications. The feature selection is used to choose the best features, and the third stage is to identify spam using a Genetic algorithm classifier, Support Vector Machine, Bayesian Classifier, Nave Bayes, SVM, Random Forest, and Long-Short Term Memory classifier.