Classification Model of Spam Emails Based on Data Mining – Deep Learning Techniques

Spam emails are unsolicited, unwanted emails that are usually sent in large quantities by advertisers and scammers.They are often sent for the purpose of promoting a product or service or for phishing, which is the attempt to obtain sensitive information such as usernames, passwords, and credit card details by pretending to be a trustworthy entity in an electronic communication.Deep learning algorithms can be used to identify spam emails by analyzing large datasets of email messages and learning to recognize patterns and trends that are indicative of spam.For example, a deep learning algorithm could be trained on a dataset of spam and non-spam emails and then be able to identify spam emails with a high degree of accuracy based on the patterns and trends it has learned from the training data.For the current work, machine learning by using the random tree is used to determine the best features with the leading deep learning hybrid Deep Neural Network Convolution Neural Network (DNN-CNN) techniques in the field of disclosure of incidental messages (spam and non-spam).The results showed that a high accuracy rate (99.8%) was obtained comparing with minimum false positive rate to the other works.