Spam Detection Utilizing Statistical-Based Bayesian Classification

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.