Email and SMS spam detection based on deep learning

Over the last decade, the overwhelming use of smartphone have made users depend on it for digital communication such as Email and SMS. Consequently, the use of email and SMS services as a medium for "two-way communication" and "one-way communication" such as notification, alerts, reminders etc., by individuals and organizations has increased tremendously. Technological advancement, cost-effectiveness, high speed acknowledgment time and viability are factors that have led to their exponential growth in the 21st century. However, these communication mediums have been vulnerable to malicious attacks called spams. The constant rise of spam across these communication medium has questioned the demand for solutions. Thus, the need for ML/DL solution which without specific instructions (rule-based codes) use algorithms and statistical models to filter spam by relying on patterns and inferences in the content of message; that is, it parse the message, learn from it and decides if it is ham or spam. This approach is economical, faster, and efficient than the other two traditional methods since it can filter huge messages in short time and the ML/DL algorithms learn from the previous messages and apply it to incoming messages. Keywords: Deep learning, Machine learning, Dense Neural Network, Spam detection.