Email spam detection using hierarchical attention hybrid deep learning method
Küçük Resim Yok
Tarih
2023
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Pergamon-Elsevier Science Ltd
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
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. Considering the escalating number of spam emails, it has become crucial to devise effective strategies for spam detection. To tackle this challenge, 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.
Açıklama
Anahtar Kelimeler
Hierarchical Attentional Hybrid Neural, Networks, Email spam detection, Natural Language Processing, FastText, Attention mechanisms, Intrusion Detection, Detection Model, Network, Classification
Kaynak
Expert Systems With Applications
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
233
