Comparison of spam classification methods based on machine learning

Research Article
Open access

Comparison of spam classification methods based on machine learning

Chengrong Wu 1* , Jianlin Wang 2
  • 1 Hongshan high school, Wuhan, China    
  • 2 Cambridge foreign language middle school, Shanghai, China    
  • *corresponding author LZCWCR@outlook.com
Published on 14 June 2023 | https://doi.org/10.54254/2755-2721/6/20230786
ACE Vol.6
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-59-1
ISBN (Online): 978-1-915371-60-7

Abstract

Wapid development of the Internet, people's use of mail continues to expand, anti-spam has become a top priority. According to the statistics of relevant departments, in 2006, the total amount of spam mails received by netizens was 50 billion, which caused an economic loss of about 10.431.5 billion yuan to the national economy. In 2007, netizens received 69.4 billion pieces of junk mail, with a loss of 18.84 billion yuan. The growth rate was 38.8 percent. Spam is the main culprit that consumes network resources. Of course, preventing spam is a long way to go. Among the types of spam received by users, the top three are online shopping spam, online money-making spam and sex toys spam, which account for 17.57%, 12.55% and 9.21% respectively. Followed by attack spam, spam containing viruses, etc. At present, anti - spam main technology and method means.

Keywords:

Classification.

Wu,C.;Wang,J. (2023). Comparison of spam classification methods based on machine learning. Applied and Computational Engineering,6,188-194.
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References

[1]. Androutsopoulos I.J. Koutsias, K.V. Chandrinos, G. Paliouras, and C.D. Spyropoulos. 2000a. An Evaluation of Naive Bayesian Anti-Spam Filtering. Proceedings of the Workshop on Machine Learning in the New Information Age, Barcelona, Spain, pages 9-17.

[2]. ATENIESE G, BURNS R, CURTMOLA R, et al. Provable data possession at untrusted stores [C]// Proceedings of the 14th ACM conference on Computer and Communication Security. New York: ACM, 2007 :598-609.

[3]. Feng Junjun, LI Li. Implementation of Machine Learning in Spam Filtering [J]. Computer Knowledge and Technology,2021,17(08):154-155.DOI:10.14004/j.cnki.ckt.2021.06

[4]. Shen ichao, Design and Implementation of mail filter system. Information and Electronic Engineering, June 2003, Volume 1, Number 2, P18-21.

[5]. T.M.Cover and PE. Hart(1968), Rates of convergence for Nearest Neighbor Procedures , inProc .HaWaii Int. Conf . on System Science

[6]. https://www.kaggle.com/datasets/ozlerhakan/spam-or-not-spam-dataset HAKAN OZLER

[7]. Hao Jie. Research on P2P Traffic Detection and Control Based on Dual Features [D]. Chengdu: University of Electronic Science and Technology of China, 2010, 25-26.

[8]. Xue Jinqi Research on spam identification and processing scheme. Sun Yat sen University 20040508

[9]. https://blog.csdn.net/tysonchiu/article/details/125485175 Google academic

[10]. https://www.jianshu.com/p/7ddcf3f996f8 jianshu

Cite this article

Wu,C.;Wang,J. (2023). Comparison of spam classification methods based on machine learning. Applied and Computational Engineering,6,188-194.

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 3rd International Conference on Signal Processing and Machine Learning

ISBN：978-1-915371-59-1(Print) / 978-1-915371-60-7(Online)
Editor：Omer Burak Istanbullu
Conference website: http://www.confspml.org
Conference date: 25 February 2023
Series: Applied and Computational Engineering
Volume number: Vol.6
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Androutsopoulos I.J. Koutsias, K.V. Chandrinos, G. Paliouras, and C.D. Spyropoulos. 2000a. An Evaluation of Naive Bayesian Anti-Spam Filtering. Proceedings of the Workshop on Machine Learning in the New Information Age, Barcelona, Spain, pages 9-17.

[2]. ATENIESE G, BURNS R, CURTMOLA R, et al. Provable data possession at untrusted stores [C]// Proceedings of the 14th ACM conference on Computer and Communication Security. New York: ACM, 2007 :598-609.

[3]. Feng Junjun, LI Li. Implementation of Machine Learning in Spam Filtering [J]. Computer Knowledge and Technology,2021,17(08):154-155.DOI:10.14004/j.cnki.ckt.2021.06

[4]. Shen ichao, Design and Implementation of mail filter system. Information and Electronic Engineering, June 2003, Volume 1, Number 2, P18-21.

[5]. T.M.Cover and PE. Hart(1968), Rates of convergence for Nearest Neighbor Procedures , inProc .HaWaii Int. Conf . on System Science

[6]. https://www.kaggle.com/datasets/ozlerhakan/spam-or-not-spam-dataset HAKAN OZLER

[7]. Hao Jie. Research on P2P Traffic Detection and Control Based on Dual Features [D]. Chengdu: University of Electronic Science and Technology of China, 2010, 25-26.

[8]. Xue Jinqi Research on spam identification and processing scheme. Sun Yat sen University 20040508

[9]. https://blog.csdn.net/tysonchiu/article/details/125485175 Google academic

[10]. https://www.jianshu.com/p/7ddcf3f996f8 jianshu

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