Spam classification based on different artificial intelligence methods

Research Article
Open access

Spam classification based on different artificial intelligence methods

Xiaoke Wang 1*
  • 1 The Ohio State University    
  • *corresponding author wang.14538@osu.edu
Published on 23 October 2023 | https://doi.org/10.54254/2755-2721/20/20231091
ACE Vol.20
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-031-8
ISBN (Online): 978-1-83558-032-5

Abstract

The increased need for social communication has led to an increase in email users, and with it more spam is being spread. In this paper, by comparing and exploring the accuracy of some supervised machine learning methods and a deep learning method called Long short-term memory (LSTM) on the problem of spam classification, this paper aims to provide more solutions for the problem of spam filtering. This paper firstly conducts an in-depth understanding and analysis of the principles of different machine learning algorithm models, which is very helpful for the following research. Then the experimental comparisons after mastering the principles of different models are conducted. Regarding the process of the research, the data set was first pre-processed to facilitate the use of different algorithm models, and then the data set was put into different models for training. Finally, by comparing the accuracy and confusion matrix, it was concluded that LSTM was used in spam classification. problem is more advantageous.

Keywords:

attention, LSTM, spam filtering

Wang,X. (2023). Spam classification based on different artificial intelligence methods. Applied and Computational Engineering,20,156-164.
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References

[1]. Yu B, Xu Z, A comparative study for content-based dynamic spam classification using four machine learning algorithms. 2008 Knowledge-Based Systems 21.4: 355-362

[2]. Feng L, Wang Y, Zuo W, Quick online spam classification method based on active and incremental learning. 2016 Journal of Intelligent & Fuzzy Systems 30.1: 17-27.

[3]. Abayomi A, Olusola, et al. A review of soft techniques for SMS spam classification: Methods, approaches and applications. 2019 Engineering Applications of Artificial Intelligence 86: 197-212.

[4]. Drucker, Wu D, Vladimir N. Support vector machines for spam categorization. 1999 IEEE Transactions on Neural networks 10.5: 1048-1054.

[5]. Shams, Rushdi, Robert E. Mercer. Supervised classification of spam emails with natural language stylometry.2016 Neural Computing and Applications 27.8: 2315-2331.

[6]. Almeida, Tiago, Renato, and Akebo Y, Machine learning methods for spamdexing detection. 2016 International Journal of Information Security Science 2.3: 86-107.

[7]. Rodrigues, Anisha P., et al. Real-time twitter spam detection and sentiment analysis using machine learning and deep learning techniques. 2022 Computational Intelligence and Neuroscience.219-232.

[8]. Yang F, An implementation of naive bayes classifier. 2018 International conference on computational science and computational intelligence. 57-68.

[9]. Breiman, Leo. Random forests.2001 Machine learning 45.1: 5-32.

[10]. Jain, Gauri, Manisha Sharma, and Basant Agarwal. Spam detection in social media using convolutional and long short-term memory neural network. 2019 Annals of Mathematics and Artificial Intelligence 85.1: 21-44.

[11]. Bassiouni M., Ali M. Ham and Spam E-Mails Classification Using Machine Learning Techniques, 2018 Journal of Applied Security Research, 13:3, 315-331.

[12]. Kim, Chanju, and Kyu-Baek Hwang. "Naive Bayes classifier learning with feature selection for spam detection in social bookmarking." Proc. Europ. Conf. on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD). 2008.

[13]. Rish, Irina. "An empirical study of the naive Bayes classifier." IJCAI 2001 workshop on empirical methods in artificial intelligence. Vol. 3. No. 22. 2001.

[14]. https://archive.ics.uci.edu/ml/datasets/SMS+Spam+Collection

Cite this article

Wang,X. (2023). Spam classification based on different artificial intelligence methods. Applied and Computational Engineering,20,156-164.

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 5th International Conference on Computing and Data Science

ISBN：978-1-83558-031-8(Print) / 978-1-83558-032-5(Online)
Editor：Roman Bauer, Marwan Omar, Alan Wang
Conference website: https://2023.confcds.org/
Conference date: 14 July 2023
Series: Applied and Computational Engineering
Volume number: Vol.20
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Yu B, Xu Z, A comparative study for content-based dynamic spam classification using four machine learning algorithms. 2008 Knowledge-Based Systems 21.4: 355-362

[2]. Feng L, Wang Y, Zuo W, Quick online spam classification method based on active and incremental learning. 2016 Journal of Intelligent & Fuzzy Systems 30.1: 17-27.

[3]. Abayomi A, Olusola, et al. A review of soft techniques for SMS spam classification: Methods, approaches and applications. 2019 Engineering Applications of Artificial Intelligence 86: 197-212.

[4]. Drucker, Wu D, Vladimir N. Support vector machines for spam categorization. 1999 IEEE Transactions on Neural networks 10.5: 1048-1054.

[5]. Shams, Rushdi, Robert E. Mercer. Supervised classification of spam emails with natural language stylometry.2016 Neural Computing and Applications 27.8: 2315-2331.

[6]. Almeida, Tiago, Renato, and Akebo Y, Machine learning methods for spamdexing detection. 2016 International Journal of Information Security Science 2.3: 86-107.

[7]. Rodrigues, Anisha P., et al. Real-time twitter spam detection and sentiment analysis using machine learning and deep learning techniques. 2022 Computational Intelligence and Neuroscience.219-232.

[8]. Yang F, An implementation of naive bayes classifier. 2018 International conference on computational science and computational intelligence. 57-68.

[9]. Breiman, Leo. Random forests.2001 Machine learning 45.1: 5-32.

[10]. Jain, Gauri, Manisha Sharma, and Basant Agarwal. Spam detection in social media using convolutional and long short-term memory neural network. 2019 Annals of Mathematics and Artificial Intelligence 85.1: 21-44.

[11]. Bassiouni M., Ali M. Ham and Spam E-Mails Classification Using Machine Learning Techniques, 2018 Journal of Applied Security Research, 13:3, 315-331.

[12]. Kim, Chanju, and Kyu-Baek Hwang. "Naive Bayes classifier learning with feature selection for spam detection in social bookmarking." Proc. Europ. Conf. on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD). 2008.

[13]. Rish, Irina. "An empirical study of the naive Bayes classifier." IJCAI 2001 workshop on empirical methods in artificial intelligence. Vol. 3. No. 22. 2001.

[14]. https://archive.ics.uci.edu/ml/datasets/SMS+Spam+Collection

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