Detect the Spam email using Deep Learning

  • Safaa S. I. Ismail, , Romany F. Mansour, †, Rasha M. Abd El-Aziz, Ahmed I. Taloba


Unsolicited email spam, often known as junk email or simply spam, is unsolicited email sent in volume. They are being used for unlawful and immoral activities, such as phishing and fraud. Sending harmful links via spam emails, which can destroy our system as well as get access to yours. Although several methods and strategies for detecting various spam emails have been established and evolved, none have proven to provide certain accuracy. Both mechanical and deep learning algorithms outperformed all other proposed models. Natural language processing (NLP) improves the performance of the prediction. The majority of previous studies into E-mail Spam filtering have focused on manually determined attributes. This research employs machine learning to categorize Spam and Not-Spam texts and emails, which adds to the existing literature. Deep Neural Network and Long Short-Term Memory frameworks were used in particular. This research will recognize those phishing emails that are fraudulent by utilizing a machine learning approach. This article will describe machine learning algorithms and implement all of them and then our large datasets, with the optimal algorithm being picked for email spam identification with the optimal sensitivity and efficiency.