Applied and Computational Engineering

- The Open Access Proceedings Series for Conferences


Proceedings of the 5th International Conference on Computing and Data Science

Series Vol. 17 , 23 October 2023


Open Access | Article

A detection research of spams based on machine learning algorithms

Zhe Liu * 1
1 KNOWLEDGE-FIRST EMPOWERMENT ACADEMY

* Author to whom correspondence should be addressed.

Applied and Computational Engineering, Vol. 17, 10-16
Published 23 October 2023. © 2023 The Author(s). Published by EWA Publishing
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Citation Zhe Liu. A detection research of spams based on machine learning algorithms. ACE (2023) Vol. 17: 10-16. DOI: 10.54254/2755-2721/17/20230902.

Abstract

The wide spread of spam has brought a lot of inconvenience and trouble to people’s work and lives. Therefore, it is of great practical significance to constantly update the methods of spam classification and filtering to improve the current situation of email use. In this paper, linear regression and logistic regression are examined to test whether a random email is spam or a normal email. The logistic regression model is based on a public data set that is estimated by calculating the number of entries in the entire set and then the probability of spam. The linear regression model is based on the data from the logistic regression model and is estimated to give a line representing the probability of spam in a given range of emails. Finally, the results of these two models clearly indicate the rampant and widespread nature of spam, which can enhance the public’s overall awareness of carefully examining unknown emails.

Keywords

spams, linear regression, logistic regression, machine learning

References

1. Wang Zheng. 2020. Research on spam filtering technology based on IMI-WNB algorithm [D]. Henan University of Technology, DOI:10.27116/d.cnki.gjzgc.2020.000609.

2. Chen Liang, Zhu Yuankai, Li Changying. 2022. Research on Spam Detection Based on HHO-KNN Optimization Algorithm [J]. Computer and Telecommunications, (09):73-77. DOI:10.15966/j.cnki.dnydx.2022.09.015.

3. Zheng Xiaoxia, Liu Chao, Zou Yu. 2010. Chinese spam text filtering based on logical regression model [J]. Journal of Heilongjiang Institute of Engineering (Natural Science Edition), 24 (04) 36-39. DOI:10.19352 j.cnki.issn1671-4679.2010.04.010.

4. Gai Xuan. 2020. Spam recognition based on cluster analysis algorithm [J]. Computer and Modernization, (10):17-22.

5. Li Yuting. 2019. Spam text classification method based on deep learning [D]. North Central University.

6. Guo Junna. 2021. Image-based spam classification based on deep learning [D]. North Central University. DOI:10.27470/d.cnki.ghbgc.2021.000812.

7. ZHANG D W, LIN X H, SOWERS M F. 2007. A two-stage functional mixed models for evaluating the effect of longitudinal covariate profile on scalar outcome. Biometrics, 63 (2): 351-362.

8. Li Qifang, Su Yufang. 2022. Research on the estimation method of partial function linear regression model under dependent conditions [J]. Application probability statistics, 38(06):904-918.

9. Tang Min, Zhang Yuhao, Deng Guoqiang. 2023. An efficient non-interactive privacy protection logic regression model [J]. Computer Engineering, 49(04):32-42+51. DOI:10.19678/j.issn.1000-3428.0065549.

10. Sun Guanglu, Qi Haoliang. 2013. Spam filtering based on online sorting logistic regression [J]. Journal of Tsinghua University (Natural Science Edition), 53(05):734-741. DOI:10.16511/j.cnki.qhdxxb.2013.05.019.

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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Volume Title
Proceedings of the 5th International Conference on Computing and Data Science
ISBN (Print)
978-1-83558-025-7
ISBN (Online)
978-1-83558-026-4
Published Date
23 October 2023
Series
Applied and Computational Engineering
ISSN (Print)
2755-2721
ISSN (Online)
2755-273X
DOI
10.54254/2755-2721/17/20230902
Copyright
© 2023 The Author(s)
Open Access
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Copyright © 2023 EWA Publishing. Unless Otherwise Stated