Spam Detection Utilizing Statistical-Based Bayesian Classification
- 10.2991/amsm-16.2016.72How to use a DOI?
- spam; statistical-based bayesian classification; content detection
Spam is one of the major problem of today's life because it causes a lot of extra expense both in network infrastructure and our individual life. Among those approaches developed to detect spam, the content-based detection technique, especially statistical-based Bayesian algorithm is important and popular. However, the basic Bayesian algorithm permits on assumption and estimation. In this paper, we proposed an improved method to increase the accuracy of the algorithm. Firstly, use actual priori probability instead of constant probability of spam. Secondly, expand the selective range and rule of tokens. Finally, add URLs and images into detection content.
- © 2016, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Xianghui Zhao AU - Yangping Zhang AU - Junkai Yi PY - 2016/05 DA - 2016/05 TI - Spam Detection Utilizing Statistical-Based Bayesian Classification BT - Proceedings of the 2016 International Conference on Applied Mathematics, Simulation and Modelling PB - Atlantis Press SP - 327 EP - 330 SN - 2352-538X UR - https://doi.org/10.2991/amsm-16.2016.72 DO - 10.2991/amsm-16.2016.72 ID - Zhao2016/05 ER -