Spam Email Filtering Leveraging Improved Text Convolutional Neural Network

Research Article
Open access

Spam Email Filtering Leveraging Improved Text Convolutional Neural Network

Dongjie Chen 1*
  • 1 Beijing Union University    
  • *corresponding author 2019240383002@buu.edu.cn
Published on 1 August 2023 | https://doi.org/10.54254/2755-2721/8/20230221
ACE Vol.8
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-63-8
ISBN (Online): 978-1-915371-64-5

Abstract

This paper outlines the development of anti-spam technology in the last two decades and introduces a novel approach using a convolution neural network (CNN) to tackle the problem of spam filtering. The study uses the TREC06c dataset, containing Chinese spam email data, the dataset is split between a training set and a test set. The paper also introduces the concept of word embeddings, which converts each word in the text into a real-valued vector, better reflecting the semantic relationships between words. The TEXT-CNN algorithm is then discussed, which applies convolutional neural networks to text data and is generated by modifying the TEXT-CNN model to improve its performance in the spam filter. The conclusion of this article is that TEXT-CNN model demonstrates great results in the task of identifying spam emails, and the classification efficiency can be further improved by introducing an attention mechanism and batch processing mechanism by improving the model. The article also provides some ideas for further improvement.

Keywords:

deep learning, convolutional neural network, spam email

Chen,D. (2023). Spam Email Filtering Leveraging Improved Text Convolutional Neural Network. Applied and Computational Engineering,8,449-454.
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References

[1]. Karim, A., Azam, S., Shanmugam, B., Kannoorpatti, K., & Alazab, M. (2019). A comprehensive survey for intelligent spam email detection. IEEE Access, 7, 168261-168295.

[2]. Cormack, G. V. (2008). Email spam filtering: A systematic review. Foundations and Trends in Information Retrieval, 1(4), 335-455.

[3]. Dada, E. G., Bassi, J. S., Chiroma, H., Adetunmbi, A. O., & Ajibuwa, O. E. (2019). Machine learning for email spam filtering: review, approaches and open research problems. Heliyon, 5(6), e01802.

[4]. Sharma, A. K., & Sahni, S. (2011). A comparative study of classification algorithms for spam email data analysis. International Journal on Computer Science and Engineering, 3(5), 1890-1895.

[5]. Ferrara, E. (2019). The history of digital spam. Communications of the ACM, 62(8), 82-91.

[6]. Hedley, S. (2006). A brief history of spam. Information & Communications Technology Law, 15(3), 223-238.

[7]. Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255-260.

[8]. Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., et al. (2018). Recent advances in convolutional neural networks. Pattern recognition, 77, 354-377.

[9]. Tang, X., Wan, Y., Liu, Y., & Cai, J. (2017, October). Chinese spam classification based on weighted distributed characteristic. In 2017 Chinese Automation Congress (CAC), 6618-6622.

[10]. Lai, S., Liu, K., He, S., & Zhao, J. (2016). How to generate a good word embedding. IEEE Intelligent Systems, 31(6), 5-14.

Cite this article

Chen,D. (2023). Spam Email Filtering Leveraging Improved Text Convolutional Neural Network. Applied and Computational Engineering,8,449-454.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 2023 International Conference on Software Engineering and Machine Learning

ISBN：978-1-915371-63-8(Print) / 978-1-915371-64-5(Online)
Editor：Anil Fernando, Marwan Omar
Conference website: http://www.confseml.org
Conference date: 19 April 2023
Series: Applied and Computational Engineering
Volume number: Vol.8
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Karim, A., Azam, S., Shanmugam, B., Kannoorpatti, K., & Alazab, M. (2019). A comprehensive survey for intelligent spam email detection. IEEE Access, 7, 168261-168295.

[2]. Cormack, G. V. (2008). Email spam filtering: A systematic review. Foundations and Trends in Information Retrieval, 1(4), 335-455.

[3]. Dada, E. G., Bassi, J. S., Chiroma, H., Adetunmbi, A. O., & Ajibuwa, O. E. (2019). Machine learning for email spam filtering: review, approaches and open research problems. Heliyon, 5(6), e01802.

[4]. Sharma, A. K., & Sahni, S. (2011). A comparative study of classification algorithms for spam email data analysis. International Journal on Computer Science and Engineering, 3(5), 1890-1895.

[5]. Ferrara, E. (2019). The history of digital spam. Communications of the ACM, 62(8), 82-91.

[6]. Hedley, S. (2006). A brief history of spam. Information & Communications Technology Law, 15(3), 223-238.

[7]. Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255-260.

[8]. Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., et al. (2018). Recent advances in convolutional neural networks. Pattern recognition, 77, 354-377.

[9]. Tang, X., Wan, Y., Liu, Y., & Cai, J. (2017, October). Chinese spam classification based on weighted distributed characteristic. In 2017 Chinese Automation Congress (CAC), 6618-6622.

[10]. Lai, S., Liu, K., He, S., & Zhao, J. (2016). How to generate a good word embedding. IEEE Intelligent Systems, 31(6), 5-14.

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