Using Artificial Neural Network to Filter Spam for Chinese Mail

Abstract:

As the Internet developed, the problem of spam has become increasingly serious. Not only caused great distress to individuals, but also have a great business costs. With improvements in computing speed, neural network is becoming a very good tool for text classification. The purpose of this study is to conduct few experiments by using neural network approach for Chinese mails’ content. The result shows that neural network approach is effective for Chinese mails’ spam-identification and the adjustments of some parameters (the number of keywords, the number of nodes, and the number of categories) also increase the accurate rate, while reducing false positives.

• Periodical: Applied Mechanics and Materials (Volumes 55-57)
• Pages: 762-766
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.55-57.762
• Online since: May 2011
• Authors: Shih Ming Pi, Hsiu Li Liao, Su Houn Liu, Ding Kang Liu
• Keywords: Chinese Mail, Neural Network (NN), Spam Detection

References

[1] E. Blanzieri and A. Bryl: A Survey of Learning-Based Techniques of Email Spam Filtering, Artificial Intelligence Review Vol. 29(1) (2008), pp.63-92.

DOI: 10.1007/s10462-009-9109-6

[2] Y. Cao, X. Liao, and Y. Li: An E-Mail Filtering Approach Using Neural Network. In ISNN Vol. 2, (2004), pp.688-694.

DOI: 10.1007/978-3-540-28648-6_110

[3] Z. Chuan, L. Xianliang, H. Mengshu, and Z. Xu: A Lvq-Based Neural Network Anti-Spam Email Approach. SIGOPS Oper. Syst. Rev. Vol. 39(1) (2005), pp.34-39.

DOI: 10.1145/1044552.1044555

[4] J. Clark, I. Koprinska, and J. Poon: A Neural Network Based Approach to Automated E-Mail Classification. Proceedings of the IEEE/WIC International Conference on Web Intelligence (WI'03) (2003), pp.702-705.

DOI: 10.1109/wi.2003.1241300

[5] T. Guzella and W. Caminhas: A Review of Machine Learning Approaches to Spam Filtering. Expert Systems with Applications Vol. 36 (2009), pp.10206-10222.

DOI: 10.1016/j.eswa.2009.02.037

[6] A. Khan, B. Baharudin, L. H. Lee, and K. Khan: A Review of Machine Learning Algorithms for Text-Documents Classification. Journal of Advances in Information Technology Vol. 1(1) (2010), pp.4-20.

DOI: 10.4304/jait.1.1.4-20

[7] A. T. Sabri, A. H. Mohammads, B. Al-Shargabi, and M. A. Hamdeh: Developing New Continuous Learning Approach for Spam Detection Using Artificial Neural Network (CLA_ANN). European Journal of Scientific Research Vol. 42(3) (2010), pp.525-535.

[8] I. Stuart, S. Cha, and C. Tappert: A neural network classifier for junk e-mail. Document Analysis Systems (2004), pp.442-450.

DOI: 10.1007/978-3-540-28640-0_42

[9] H. Xu and B. Yu: Automatic Thesaurus Construction for Spam Filtering Using Revised Back Propagation Neural Network. Expert Systems with Applications Vol. 37(1) (2010), pp.18-23.

DOI: 10.1016/j.eswa.2009.02.059

[10] Y. Yang and S. A.: Elfayoumy. Anti-spam filtering using neural networks and Baysian classifiers. IEEE International Symposium on Computational Intelligence in Robotics and Automation (2007), pp.272-278.

DOI: 10.1109/cira.2007.382929

[11] B. Yu, and Z. B. Xu: A Comparative Study for Content-Based Dynamic Spam Classification Using Four Machine Learning Algorithms. Knowledge-Based Systems Vol. 21(4) (2008), pp.355-362.

DOI: 10.1016/j.knosys.2008.01.001