Proceedings of the 2016 International Conference on Applied Mathematics, Simulation and Modelling

Spam Detection Utilizing Statistical-Based Bayesian Classification

Authors
Xianghui Zhao, Yangping Zhang, Junkai Yi
Corresponding Author
Xianghui Zhao
Available Online May 2016.
DOI
10.2991/amsm-16.2016.72How to use a DOI?
Keywords
spam; statistical-based bayesian classification; content detection
Abstract

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.

Copyright
© 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/).

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Volume Title
Proceedings of the 2016 International Conference on Applied Mathematics, Simulation and Modelling
Series
Advances in Computer Science Research
Publication Date
May 2016
ISBN
978-94-6252-198-8
ISSN
2352-538X
DOI
10.2991/amsm-16.2016.72How to use a DOI?
Copyright
© 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  -