  Email is one of the most widely used ways to communicate, with millions of
people and businesses relying on it to communicate and share knowledge and
information on a daily basis. Nevertheless, the rise in email users has
occurred a dramatic increase in spam emails in recent years. Processing and
managing emails properly for individuals and companies are getting increasingly
difficult. This article proposes a novel technique for email spam detection
that is based on a combination of convolutional neural networks, gated
recurrent units, and attention mechanisms. During system training, the network
is selectively focused on necessary parts of the email text. The usage of
convolution layers to extract more meaningful, abstract, and generalizable
features by hierarchical representation is the major contribution of this
study. Additionally, this contribution incorporates cross-dataset evaluation,
which enables the generation of more independent performance results from the
model's training dataset. According to cross-dataset evaluation results, the
proposed technique advances the results of the present attention-based
techniques by utilizing temporal convolutions, which give us more flexible
receptive field sizes are utilized. The suggested technique's findings are
compared to those of state-of-the-art models and show that our approach
outperforms them.
