Issue |
ITM Web Conf.
Volume 70, 2025
2024 2nd International Conference on Data Science, Advanced Algorithm and Intelligent Computing (DAI 2024)
|
|
---|---|---|
Article Number | 03027 | |
Number of page(s) | 6 | |
Section | Image Processing and Computer Vision | |
DOI | https://doi.org/10.1051/itmconf/20257003027 | |
Published online | 23 January 2025 |
The effect of using Naive Bayes to detect spam email
Department of EIE, The Hong Kong Polytechnic University, Hong Kong, China
Corresponding author: 23104993d@connect.polyu.hk
The rapid growth of the Internet has made email a huge help in business, life, and education. However, it has also become a channel for spreading undesirable information, such as content from hackers, viruses, violence, pornography, and superstition. Spam, which is unsolicited commercial email, often carries such undesirable information. It wastes network bandwidth, consumes users’ precious time, and interferes with normal life. Therefore, spam detection and filtering have become especially urgent and of great practical importance. This paper focuses on the spam detection method based on the plain Bayesian algorithm. The plain Bayesian algorithm is particularly suitable for spam detection due to its high detection accuracy and its wide application in text classification tasks. The results and analysis of the experimental dataset demonstrate that the accuracy of Park’s Bayesian algorithm in spam detection reaches an impressive 99.193%. This high level of accuracy underscores the effectiveness of the Bayesian approach in identifying and filtering out spam, thereby enhancing the overall efficiency and security of email communication.
© The Authors, published by EDP Sciences, 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.