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Email is one of the most widely used ways to communicate, with millions ofpeople and businesses relying on it to communicate and share knowledge andinformation on a daily basis. Nevertheless, the rise in email users hasoccurred a dramatic increase in spam emails in recent years. Processing andmanaging emails properly for individuals and companies are getting increasinglydifficult. This article proposes a novel technique for email spam detectionthat is based on a combination of convolutional neural networks, gatedrecurrent units, and attention mechanisms. During system training, the networkis selectively focused on necessary parts of the email text. The usage ofconvolution layers to extract more meaningful, abstract, and generalizablefeatures by hierarchical representation is the major contribution of thisstudy. Additionally, this contribution incorporates cross-dataset evaluation,which enables the generation of more independent performance results from themodel's training dataset. According to cross-dataset evaluation results, theproposed technique advances the results of the present attention-basedtechniques by utilizing temporal convolutions, which give us more flexiblereceptive field sizes are utilized. The suggested technique's findings arecompared to those of state-of-the-art models and show that our approachoutperforms them.

Email Spam Detection Using Hierarchical Attention Hybrid Deep Learning Method