Classification of Spam Emails using Deep learning

The Internet has become an integral part of modern life. One of the most critical aspects of the Internet is collaboration. Email is a communication tool that can be used for both personal and professional purposes. Spam messages are not intended to be received by addressee of emails, and therefore are often regarded as unwanted bulk emails. Every day, a wide range of people use email to connect globally. Currently, large numbers of Spam emails are logic genes. Being in large quantities already causes real frustration for both internet users and providers. For instance, it degrades user analysis data, encourages network virus migration, expands stack on arrangement movement, absorbs mail server storage, wastes time and network bandwidth, and depletes the vitality of real emails among the Spam. It is therefore necessary to prevent the spread of Spam. Given the fact that there are several data mining techniques beneficial in preserving security, they can also be of use in classifying Spam email. As for the present work, the Min-hash technique is combined with the Deep Neural Network (DNN) algorithm to classify emails into Spam and Ham. The results indicate that a remarkably high accuracy rate (98%) is obtained by using this combination, which means that it is an effective method to be adopted and further developed in the field of Spam detection and classification.