A spam message detection model based on Bayesian classification

Yitao Yang*, Runqiu Hu, Chengyan Qiu, Guozi Sun, Huakang Li

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingChapterpeer-review

3 Citations (Scopus)


In recent years, we have witnessed a dramatic growth in spam mail. Other related forms of spam are also increasingly exposed the seriousness of the problem, especially in the short message service (SMS). Just like spam mail, the problem of spam message can be solved with legal, economic or technical means. Among the technical means, Bayesian classification algorithm, which is simple to design and has the higher accuracy, becomes the most effective filtration methods. In addition, from the perspective of social development, digital evidence will play an important role in legal practice in the future. Therefore, spam message, a kind of digital evidence, will also become the main relevant evidence to the case. This paper presents a spam message detection model based on the Bayesian classification algorithm. And it will be applied to the process of SMS forensics as a means to analyze and identify the digital evidence. Test results show that the system can effectively detect spam messages, so it will play a great role in judging criminal suspects, and it can be used as a workable scheme in SMS forensics.

Original languageEnglish
Title of host publicationLecture Notes on Data Engineering and Communications Technologies
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages12
Publication statusPublished - 2018
Externally publishedYes

Publication series

NameLecture Notes on Data Engineering and Communications Technologies
ISSN (Print)2367-4512
ISSN (Electronic)2367-4520


  • Bayesian classification
  • Digital forensics
  • Machine learning
  • Spam message filtering


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