Chinese Spam Filtering Based On Back-Propagation Neural Networks

Peiguo Li; Yan Ye

doi:doi:10.11648/j.se.20160402.11

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Chinese Spam Filtering Based On Back-Propagation Neural Networks

Peiguo Li, Yan Ye

Published in Software Engineering (Volume 4, Issue 2)

Received: 15 April 2016 Published: 16 April 2016

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Abstract

As the email service is becoming an important communication way on the Network, the spam is increasing every day. This paper describes a new filtering model based on email content by using Back-Propagation Neural Networks (BPNN). And for the Chinese email, it uses Natural Language Processing & Information Retrieval Sharing Platform (NLPIR) system to perform Chinese word segmentation. The simulation results show that this model can precisely filter the Chinese spam.

Published in	Software Engineering (Volume 4, Issue 2)
DOI	10.11648/j.se.20160402.11
Page(s)	9-12
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2016. Published by Science Publishing Group

Keywords

Spam, BPNN, NLPIR

References

[1]	https://en.wikipedia.org/wiki/Email_spam.
[2]	http://www.informationweek.com/spam-costs-billions/d/d-id/1030111.
[3]	Ismaila Idris, Ali Selamat and Sigeru Omatu, “Hybrid email spam detection model with negative selection algorithm and differential evolution”, Engineering Applications of Artificial Intelligence, Volume 28, February 2014, pp. 97–110.
[4]	Ismaila Idrisa, Ali Selamat, “A combined negative selection algorithm–particle swarm optimization for an email spam detection system”, Engineering Applications of Artificial Intelligence, Volume 39, March 2015, Pages 33–44.
[5]	Atefeh Heydaria, Mohammad ali Tavakolia,, “Detection of review spam: A survey”, Expert Systems with Applications, Volume 42, Issue 7, 1 May 2015, Pages 3634–3642.
[6]	M. Sahami, S. Dumais, D. Heckerman and E.A. Horvitz, “Bayesian approach to filtering junk email”, Proc. of AAAI’98 Workshop on Learning for Text Categorization, Madison, WI, July (1998), pp. 55–62.
[7]	X. Carreras and L. Marquez, “Boosting trees for anti-spam email filtering”, Proc. of Fourth Int. Conf. on Recent Advances in Natural Language Processing, Tzigov Chark, Bulgaria, September (2001).
[8]	Zhao Wenqing and Zhang Zili, “An email classification model based on rough set theory”, (AMT 2005). Proceedings of the 2005 International Conference on Active Media Technology.
[9]	J. Clark, I. Koprinska, J. Poon, “A neural network based approach to automated email classification”, Proc. of the IEEE/WIC Int. Conf. on Web Intelligence (WI’03) (2003).
[10]	M. M. Fuad, D. Deb, M. S. Hossain, “A trainable fuzzy spam detection system”, Proc. of the 7th Int. Conf. on Computer and Information Technology (2004).
[11]	http://ictclas.nlpir.org/docs
[12]	https://sourceforge.net/p/joone/wiki/Home/
[13]	https://sourceforge.net/p/jgap/wiki/Home/

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APA Style

Peiguo Li, Yan Ye. (2016). Chinese Spam Filtering Based On Back-Propagation Neural Networks. Software Engineering, 4(2), 9-12. https://doi.org/10.11648/j.se.20160402.11

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ACS Style

Peiguo Li; Yan Ye. Chinese Spam Filtering Based On Back-Propagation Neural Networks. Softw. Eng. 2016, 4(2), 9-12. doi: 10.11648/j.se.20160402.11

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AMA Style

Peiguo Li, Yan Ye. Chinese Spam Filtering Based On Back-Propagation Neural Networks. Softw Eng. 2016;4(2):9-12. doi: 10.11648/j.se.20160402.11

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@article{10.11648/j.se.20160402.11,
  author = {Peiguo Li and Yan Ye},
  title = {Chinese Spam Filtering Based On Back-Propagation Neural Networks},
  journal = {Software Engineering},
  volume = {4},
  number = {2},
  pages = {9-12},
  doi = {10.11648/j.se.20160402.11},
  url = {https://doi.org/10.11648/j.se.20160402.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.se.20160402.11},
  abstract = {As the email service is becoming an important communication way on the Network, the spam is increasing every day. This paper describes a new filtering model based on email content by using Back-Propagation Neural Networks (BPNN). And for the Chinese email, it uses Natural Language Processing & Information Retrieval Sharing Platform (NLPIR) system to perform Chinese word segmentation. The simulation results show that this model can precisely filter the Chinese spam.},
 year = {2016}
}

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TY  - JOUR
T1  - Chinese Spam Filtering Based On Back-Propagation Neural Networks
AU  - Peiguo Li
AU  - Yan Ye
Y1  - 2016/04/16
PY  - 2016
N1  - https://doi.org/10.11648/j.se.20160402.11
DO  - 10.11648/j.se.20160402.11
T2  - Software Engineering
JF  - Software Engineering
JO  - Software Engineering
SP  - 9
EP  - 12
PB  - Science Publishing Group
SN  - 2376-8037
UR  - https://doi.org/10.11648/j.se.20160402.11
AB  - As the email service is becoming an important communication way on the Network, the spam is increasing every day. This paper describes a new filtering model based on email content by using Back-Propagation Neural Networks (BPNN). And for the Chinese email, it uses Natural Language Processing & Information Retrieval Sharing Platform (NLPIR) system to perform Chinese word segmentation. The simulation results show that this model can precisely filter the Chinese spam.
VL  - 4
IS  - 2
ER  -

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Author Information

Peiguo Li

Department of Mathematics, Jinan University, Guangzhou, China
Yan Ye

Department of Computer Science, Guangzhou College of Commerce, Guangzhou, China

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Plain Text BibTeX RIS

APA Style

Peiguo Li, Yan Ye. (2016). Chinese Spam Filtering Based On Back-Propagation Neural Networks. Software Engineering, 4(2), 9-12. https://doi.org/10.11648/j.se.20160402.11

Copy | Download

ACS Style

Peiguo Li; Yan Ye. Chinese Spam Filtering Based On Back-Propagation Neural Networks. Softw. Eng. 2016, 4(2), 9-12. doi: 10.11648/j.se.20160402.11

Copy | Download

AMA Style

Peiguo Li, Yan Ye. Chinese Spam Filtering Based On Back-Propagation Neural Networks. Softw Eng. 2016;4(2):9-12. doi: 10.11648/j.se.20160402.11

Copy | Download

@article{10.11648/j.se.20160402.11,
  author = {Peiguo Li and Yan Ye},
  title = {Chinese Spam Filtering Based On Back-Propagation Neural Networks},
  journal = {Software Engineering},
  volume = {4},
  number = {2},
  pages = {9-12},
  doi = {10.11648/j.se.20160402.11},
  url = {https://doi.org/10.11648/j.se.20160402.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.se.20160402.11},
  abstract = {As the email service is becoming an important communication way on the Network, the spam is increasing every day. This paper describes a new filtering model based on email content by using Back-Propagation Neural Networks (BPNN). And for the Chinese email, it uses Natural Language Processing & Information Retrieval Sharing Platform (NLPIR) system to perform Chinese word segmentation. The simulation results show that this model can precisely filter the Chinese spam.},
 year = {2016}
}

Copy | Download

TY  - JOUR
T1  - Chinese Spam Filtering Based On Back-Propagation Neural Networks
AU  - Peiguo Li
AU  - Yan Ye
Y1  - 2016/04/16
PY  - 2016
N1  - https://doi.org/10.11648/j.se.20160402.11
DO  - 10.11648/j.se.20160402.11
T2  - Software Engineering
JF  - Software Engineering
JO  - Software Engineering
SP  - 9
EP  - 12
PB  - Science Publishing Group
SN  - 2376-8037
UR  - https://doi.org/10.11648/j.se.20160402.11
AB  - As the email service is becoming an important communication way on the Network, the spam is increasing every day. This paper describes a new filtering model based on email content by using Back-Propagation Neural Networks (BPNN). And for the Chinese email, it uses Natural Language Processing & Information Retrieval Sharing Platform (NLPIR) system to perform Chinese word segmentation. The simulation results show that this model can precisely filter the Chinese spam.
VL  - 4
IS  - 2
ER  -

Copy | Download