A detection research of spams based on machine learning algorithms

Zhe Liu

doi:10.54254/2755-2721/17/20230902

Research Article

Open access

Published on 23 October 2023

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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.

https://doi.org/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

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References

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Cite this article

Liu,Z. (2023). A detection research of spams based on machine learning algorithms. Applied and Computational Engineering,17,10-16.

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

Conference website: https://2023.confcds.org/

ISBN：978-1-83558-025-7(Print) / 978-1-83558-026-4(Online)

Conference date: 14 July 2023

Editor：Roman Bauer, Marwan Omar, Alan Wang

Series: Applied and Computational Engineering

Volume number: Vol.17

ISSN：2755-2721(Print) / 2755-273X(Online)

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