Ahmed, Md. Tofael and Akter, Mariam and Rahman, Md. Saifur and Rahman, Maqsudur and Paul, Pintu Chandra and Parvin, Miss. Nargis and Antar, Almas Hossain (2023) Deep Neural Network Based Spam Email Classification Using Attention Mechanisms. Journal of Intelligent Learning Systems and Applications, 15 (04). pp. 144-164. ISSN 2150-8402
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Abstract
Spam emails pose a threat to individuals. The proliferation of spam emails daily has rendered traditional machine learning and deep learning methods for screening them ineffective and inefficient. In our research, we employ deep neural networks like RNN, LSTM, and GRU, incorporating attention mechanisms such as Bahdanua, scaled dot product (SDP), and Luong scaled dot product self-attention for spam email filtering. We evaluate our approach on various datasets, including Trec spam, Enron spam emails, SMS spam collections, and the Ling spam dataset, which constitutes a substantial custom dataset. All these datasets are publicly available. For the Enron dataset, we attain an accuracy of 99.97% using LSTM with SDP self-attention. Our custom dataset exhibits the highest accuracy of 99.01% when employing GRU with SDP self-attention. The SMS spam collection dataset yields a peak accuracy of 99.61% with LSTM and SDP attention. Using the GRU (Gated Recurrent Unit) alongside Luong and SDP (Structured Self-Attention) attention mechanisms, the peak accuracy of 99.89% in the Ling spam dataset. For the Trec spam dataset, the most accurate results are achieved using Luong attention LSTM, with an accuracy rate of 99.01%. Our performance analyses consistently indicate that employing the scaled dot product attention mechanism in conjunction with gated recurrent neural networks (GRU) delivers the most effective results. In summary, our research underscores the efficacy of employing advanced deep learning techniques and attention mechanisms for spam email filtering, with remarkable accuracy across multiple datasets. This approach presents a promising solution to the ever-growing problem of spam emails.
Item Type: | Article |
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Subjects: | Science Global Plos > Social Sciences and Humanities |
Depositing User: | Unnamed user with email support@science.globalplos.com |
Date Deposited: | 15 Dec 2023 04:50 |
Last Modified: | 15 Dec 2023 04:50 |
URI: | http://ebooks.manu2sent.com/id/eprint/2384 |