Spam Detection Utilizing Statistical-Based Bayesian Classification

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/).

Download article (PDF)

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  -